RobjHunt
Regular
Your sarcasm is terribly inforbidable
BRN Discussion Ongoing
- Thread starter TechGirl
- Start date
RobjHunt
Regular
Fair chance that he doesn’t. He’s a Kiwi after all
FiveBucks
Regular
I just used Gemini's newest Deep Research AI model to "Do My Own Research" for me 
. Asking if it believes Brainchip will be a successful company in the future.
The AI model probably used enough power to run a small town for a month (sorry in advance to my future grand children) but if interested, see results below.
BrainChip Holdings Ltd (ASX:BRN), an Australian-listed entity, is positioned as a pioneer in the nascent field of neuromorphic artificial intelligence (AI), focusing on the commercialization of its Akida™ processor technology through an intellectual property (IP) licensing model for the edge computing market. This report provides an in-depth analysis of BrainChip's core technology, its target markets, competitive landscape, business model, financial health, and associated risks. The objective is to assess the company's potential for commercial success and analyze factors likely to influence its market capitalization over the next 12 months, providing a comprehensive evaluation for informed investors.
The analysis indicates that BrainChip's Akida technology possesses unique potential advantages, particularly its event-based processing architecture designed for ultra-low power consumption and its capability for on-chip learning. These features align well with the demands of the rapidly expanding Edge AI market. However, the company faces substantial hurdles in translating this technological promise into significant commercial traction and revenue. The neuromorphic and edge AI markets, while large and growing, are intensely competitive, featuring established semiconductor giants and numerous specialized startups. BrainChip's financial performance to date is characterized by minimal revenue generation and a significant rate of cash expenditure, necessitating continuous reliance on external funding mechanisms and resulting in shareholder dilution. Key partnerships, including those with Renesas, MegaChips, Frontgrade Gaisler, and a notable collaboration with Mercedes-Benz on a concept vehicle, provide crucial technological validation but have yet to yield substantial, recurring royalty revenues, which are fundamental to the long-term success of its IP licensing model. Recent commercial engagements appear concentrated in lower-volume, albeit high-profile, defense and space sectors.
The outlook for BrainChip remains highly speculative. Its future success is critically dependent on achieving significant commercial milestones in the near term, particularly securing high-volume licensing agreements that translate into meaningful royalty streams. The timely delivery and market adoption of its second-generation Akida 2.0 platform, which promises enhanced capabilities required for more complex AI tasks, is paramount. BrainChip's market capitalization over the next 12 months is expected to be highly sensitive to news flow regarding commercial contracts, progress on the Akida 2.0 roadmap, developments from key licensees like Renesas, funding activities, and the potential execution of its proposed US redomiciling. Given the early stage of commercialization and the inherent uncertainties in technology adoption and market dynamics, predictions regarding stock performance carry a high degree of risk.
1. Introduction: BrainChip Holdings (ASX:BRN) - Pioneering Neuromorphic AI
BrainChip Holdings Ltd, trading under the tickers ASX:BRN, OTCQX:BRCHF, and ADR:BCHPY, is a technology company headquartered in Australia with significant operations in the United States. The company has positioned itself at the forefront of neuromorphic computing, aiming to be the worldwide leader in edge AI on-chip processing and learning.1 Its core focus is the development and commercialization of its proprietary Akida™ neuromorphic System-on-Chip (NSoC) technology. BrainChip primarily operates under an Intellectual Property (IP) licensing business model, providing its Akida architecture to semiconductor manufacturers and Original Equipment Manufacturers (OEMs) for integration into their own products.5 The company frequently emphasizes its status as the "world's first commercial producer" of fully digital, event-based neuromorphic AI IP.1
This report aims to provide a comprehensive evaluation of BrainChip's prospects by examining eight critical areas:
2.1. Core Principles: Event-Based Processing and Spiking Neural Networks (SNNs)
Neuromorphic computing represents a fundamental departure from conventional computer architectures. Instead of the traditional von Neumann model with separate processing and memory units synchronized by a clock, neuromorphic systems draw inspiration from the biological brain's structure and function.5 These systems aim to replicate the way neurons and synapses process information, often utilizing asynchronous, event-driven communication.
BrainChip's Akida™ technology embodies this philosophy through a fully digital, event-based architecture.1 This is a crucial design choice. Being fully digital allows Akida IP to be designed using standard Electronic Design Automation (EDA) tools and synthesized into RTL, making it compatible with standard semiconductor manufacturing processes at foundries like TSMC, GlobalFoundries, and potentially Intel Foundry Services.12 This approach significantly lowers the barrier to adoption for semiconductor companies compared to analog or mixed-signal neuromorphic designs, reducing manufacturing risk and potentially cost.
The core operational principle of Akida is event-based processing. It analyzes sensor data at the point of acquisition, processing only essential information triggered by changes or "events" – often represented as "spikes".1 By leveraging the inherent sparsity in real-world data (i.e., the fact that often, not much changes from one moment to the next), Akida aims to dramatically reduce the number of computations required compared to conventional AI accelerators that process entire frames or data blocks continuously.5 This principle is the foundation for Akida's primary claimed benefit: ultra-low power consumption.1 Akida utilizes Spiking Neural Networks (SNNs), which process information encoded in the timing of these spikes, mirroring biological neural processing more closely than traditional Artificial Neural Networks (ANNs). A key part of BrainChip's offering is the capability within its software tools to convert conventional ANNs, such as Convolutional Neural Networks (CNNs), into SNNs that can run efficiently on the Akida hardware.17
2.2. Key Differentiators: Ultra-Low Power, On-Chip Learning, and Efficiency
BrainChip highlights three key differentiators for its Akida technology: ultra-low power consumption, unique on-chip learning capabilities, and high processing efficiency.
The claim of ultra-low power consumption is central to Akida's value proposition.1 By processing only sparse, event-based data, the architecture is designed to consume significantly less energy than traditional AI accelerators. Specific power figures cited include approximately 30 mW for the core technology 21, around 1 watt for an M.2 module incorporating the AKD1000 chip 1, and sub-milliwatt operation for the Akida Pico IP core designed for extremely constrained devices.11 One benchmark comparison suggested Akida processed the ImageNet dataset using 300 milliwatts versus 300 watts for a GPU, although such comparisons require careful contextualization.20 Another benchmark against Intel's Loihi 2 showed Akida 1000 consuming 1W compared to Loihi 2's 2.5W on a cybersecurity task, despite Akida being on an older process node.24 This low-power characteristic is positioned as enabling AI functionality in edge devices where energy budgets are severely limited, such as battery-powered sensors, wearables, and various IoT applications.11
A second major differentiator is on-chip learning.1 Akida is designed to allow AI models to learn and adapt after deployment, directly on the device, without requiring connection to the cloud for retraining.5 This capability is enabled by integrating learning rules, reportedly inspired by Spike-Timing-Dependent Plasticity (STDP), directly into the hardware.10 The potential benefits are significant for edge applications:
Finally, BrainChip emphasizes efficiency and performance. The Akida architecture aims to minimize data movement – a primary source of power consumption in conventional AI systems – by placing entire neural networks onto the hardware fabric, reducing or eliminating the need to constantly swap weights and activations in and out of external memory (DRAM).7 The company claims this results in high throughput and precision with unparalleled energy efficiency across various data modalities, including vision, audio, and other sensor data.1
2.3. Akida Generations (1.0 vs. 2.0) and Product Implementations
The Akida architecture is designed to be scalable, configurable from 2 up to 256 interconnected nodes, allowing customization for different performance and power requirements.5 Each node comprises four Neural Processing Engines (NPEs), which can be configured as convolutional or fully connected layers, supported by local SRAM.12
The Akida 1.0 platform is based on the initial architecture, embodied in the AKD1000 chip (featuring a claimed 1.2 million neurons and 10 billion synapses 12) released in 2022 21, and the subsequent AKD1500. Akida 1.0 technology is available in several forms:
However, there appears to be a delay in the hardware realization of Akida 2.0. While the platform was announced as available for engagement in October 2023 15, and general availability was previously targeted for Q3 2023 16, the AKD2000 hardware (presumably the silicon implementation of Gen 2) was noted as not yet released or available for purchase as of late 2024/early 2025.21 Although the MetaTF SDK has been updated to support Akida 2.0 features 21 and the company is engaging with lead adopters 16, this lag between the platform/IP availability and the corresponding purchasable hardware represents an execution risk. Delays in delivering Akida 2.0 silicon could create an opportunity for competitors to solidify their positions in the market for advanced edge AI processing.
2.4. Software and Development Ecosystem (MetaTF, TENNs, ViT Support)
A crucial element for the adoption of any novel hardware architecture is a robust and user-friendly software development environment. BrainChip addresses this through its MetaTF Software Development Kit (SDK).6 MetaTF is designed to integrate with standard AI workflows, specifically leveraging TensorFlow and Keras.1 This approach aims to lower the adoption barrier for the large community of developers already familiar with these tools, rather than requiring them to learn entirely new languages or frameworks specific to neuromorphic computing.19
MetaTF includes key components to bridge the gap between conventional ANN development and Akida's SNN hardware 21:
Similarly, Akida 2.0 includes enhanced support for Vision Transformers (ViTs).16 ViTs have become highly influential in computer vision, and the ability to accelerate them efficiently on low-power edge devices is a significant capability. BrainChip aims to leverage Akida's architecture to run these complex models effectively at the edge.16
To further bolster its ecosystem, BrainChip has pursued partnerships with key players in the AI development space, such as integrating with the Edge Impulse platform for ML development and deployment 15 and joining the Arm AI Partner Program.15 These collaborations aim to make Akida technology accessible and usable within broader development environments.
3. Market Landscape and Opportunity
BrainChip targets two overlapping but distinct market segments: the specific Neuromorphic Computing market and the broader Edge AI market.
3.1. The Neuromorphic Computing Market: Size, Growth Projections, and Trends
The neuromorphic computing market encompasses hardware and software systems designed based on principles of biological neural structures.10 It represents a paradigm shift from conventional computing, promising significant gains in energy efficiency and potentially new computational capabilities.
However, assessing the size and growth of this market is challenging due to its nascent stage and varying definitions used by market research firms. Forecasts exhibit significant discrepancies, highlighting the uncertainty surrounding this specific segment.
Table 1: Neuromorphic Market Forecast Comparison
Note: Forecasts vary dramatically, likely due to differing definitions and methodologies.
As Table 1 illustrates, 2024 market size estimates range from a niche $28.5 million 34 to a substantial $6.9 billion.25 Projections for 2030-2034 diverge even more drastically, from $1.3 billion 34 to over $55 billion.37 While most forecasts predict strong growth (CAGRs from 21% to nearly 90%), the vast range makes it difficult to pinpoint the true current market size or reliable future value based solely on the "neuromorphic" label.
Key drivers identified across reports include the increasing demand for AI and Machine Learning capabilities, the need for more power-efficient computing alternatives to traditional methods, requirements for real-time data processing, and the growth of IoT and edge devices.25 Hardware (chips) currently dominates the market revenue, though software is expected to grow rapidly.25 Edge deployment is the primary model targeted.25 Geographically, North America holds the largest share, driven by major tech players and investments, while the Asia Pacific region is projected to exhibit the fastest growth.25 Dominant applications include image and video processing/computer vision, with data processing, signal processing, and sensor fusion also being significant.25 Key end-use verticals mirror those targeted by BrainChip: consumer electronics, automotive, industrial, healthcare, IT/telecom, and aerospace/defense.25
The significant ambiguity in market sizing suggests that investors should be cautious about relying solely on "neuromorphic computing" market forecasts to gauge BrainChip's opportunity. The company's success is more likely to be determined by its ability to capture share within the larger, better-defined Edge AI market, using its neuromorphic technology as a key differentiator.
3.2. The Edge AI Market: Size, Growth Projections, and Key Drivers
The Edge AI market involves deploying and running AI algorithms directly on end-user devices or local edge servers, rather than relying on centralized cloud data centers.19 This approach offers compelling advantages for many applications, including significantly reduced latency for real-time responses, enhanced data privacy and security by keeping data local, reduced bandwidth consumption and cost, and improved reliability in environments with intermittent connectivity.1
This market is substantially larger and more clearly defined than the niche neuromorphic segment, representing a significant opportunity for technologies like Akida. Market forecasts consistently point to a large and rapidly growing sector.
Table 2: Edge AI Market Forecast Comparison
Note: Variations exist based on scope (full market vs. accelerators) and methodology. CAGR calculations may vary based on exact start/end points.
Table 2 shows that the Edge AI market was already valued in the tens of billions USD in 2024/2025, with forecasts projecting significant growth, reaching anywhere from approximately $66 billion to over $350 billion by 2030-2035.38 Projected CAGRs generally range from around 9% to over 30%, indicating robust expansion.38
The primary drivers fueling this growth are 36:
BrainChip's core value proposition – ultra-low power consumption, low latency processing, and on-device learning – directly addresses key challenges and requirements within this large and expanding Edge AI market.1 This strong alignment validates the strategic importance of the market opportunity for BrainChip. The primary challenge lies in execution: successfully competing against established players and translating technological potential into significant market share and revenue within this dynamic landscape.
3.3. Target Applications and Verticals
The specific features of the Akida processor – its event-based nature, low power draw, and on-chip learning – make it potentially suitable for a wide spectrum of edge AI applications where these characteristics are advantageous. BrainChip highlights numerous use cases across various industry verticals 1:
. Asking if it believes Brainchip will be a successful company in the future.The AI model probably used enough power to run a small town for a month (sorry in advance to my future grand children) but if interested, see results below.
BrainChip Holdings Ltd (ASX:BRN): Analysis of Neuromorphic Technology, Market Position, and Commercial Prospects
Executive SummaryBrainChip Holdings Ltd (ASX:BRN), an Australian-listed entity, is positioned as a pioneer in the nascent field of neuromorphic artificial intelligence (AI), focusing on the commercialization of its Akida™ processor technology through an intellectual property (IP) licensing model for the edge computing market. This report provides an in-depth analysis of BrainChip's core technology, its target markets, competitive landscape, business model, financial health, and associated risks. The objective is to assess the company's potential for commercial success and analyze factors likely to influence its market capitalization over the next 12 months, providing a comprehensive evaluation for informed investors.
The analysis indicates that BrainChip's Akida technology possesses unique potential advantages, particularly its event-based processing architecture designed for ultra-low power consumption and its capability for on-chip learning. These features align well with the demands of the rapidly expanding Edge AI market. However, the company faces substantial hurdles in translating this technological promise into significant commercial traction and revenue. The neuromorphic and edge AI markets, while large and growing, are intensely competitive, featuring established semiconductor giants and numerous specialized startups. BrainChip's financial performance to date is characterized by minimal revenue generation and a significant rate of cash expenditure, necessitating continuous reliance on external funding mechanisms and resulting in shareholder dilution. Key partnerships, including those with Renesas, MegaChips, Frontgrade Gaisler, and a notable collaboration with Mercedes-Benz on a concept vehicle, provide crucial technological validation but have yet to yield substantial, recurring royalty revenues, which are fundamental to the long-term success of its IP licensing model. Recent commercial engagements appear concentrated in lower-volume, albeit high-profile, defense and space sectors.
The outlook for BrainChip remains highly speculative. Its future success is critically dependent on achieving significant commercial milestones in the near term, particularly securing high-volume licensing agreements that translate into meaningful royalty streams. The timely delivery and market adoption of its second-generation Akida 2.0 platform, which promises enhanced capabilities required for more complex AI tasks, is paramount. BrainChip's market capitalization over the next 12 months is expected to be highly sensitive to news flow regarding commercial contracts, progress on the Akida 2.0 roadmap, developments from key licensees like Renesas, funding activities, and the potential execution of its proposed US redomiciling. Given the early stage of commercialization and the inherent uncertainties in technology adoption and market dynamics, predictions regarding stock performance carry a high degree of risk.
1. Introduction: BrainChip Holdings (ASX:BRN) - Pioneering Neuromorphic AI
BrainChip Holdings Ltd, trading under the tickers ASX:BRN, OTCQX:BRCHF, and ADR:BCHPY, is a technology company headquartered in Australia with significant operations in the United States. The company has positioned itself at the forefront of neuromorphic computing, aiming to be the worldwide leader in edge AI on-chip processing and learning.1 Its core focus is the development and commercialization of its proprietary Akida™ neuromorphic System-on-Chip (NSoC) technology. BrainChip primarily operates under an Intellectual Property (IP) licensing business model, providing its Akida architecture to semiconductor manufacturers and Original Equipment Manufacturers (OEMs) for integration into their own products.5 The company frequently emphasizes its status as the "world's first commercial producer" of fully digital, event-based neuromorphic AI IP.1
This report aims to provide a comprehensive evaluation of BrainChip's prospects by examining eight critical areas:
- The core Akida™ technology, its applications, and competitive advantages.
- The size, growth, and trends of the target neuromorphic and edge AI markets.
- BrainChip's business model, commercialization strategy, partnerships, and customer pipeline.
- The company's recent financial performance and overall financial health.
- Historical stock performance, analyst perspectives (where available), and news sentiment.
- The competitive landscape, including key rivals in AI and neuromorphic processing.
- Key risks and challenges facing the company.
- A synthesized outlook on success potential and factors influencing market capitalization over the next 12 months.
2.1. Core Principles: Event-Based Processing and Spiking Neural Networks (SNNs)
Neuromorphic computing represents a fundamental departure from conventional computer architectures. Instead of the traditional von Neumann model with separate processing and memory units synchronized by a clock, neuromorphic systems draw inspiration from the biological brain's structure and function.5 These systems aim to replicate the way neurons and synapses process information, often utilizing asynchronous, event-driven communication.
BrainChip's Akida™ technology embodies this philosophy through a fully digital, event-based architecture.1 This is a crucial design choice. Being fully digital allows Akida IP to be designed using standard Electronic Design Automation (EDA) tools and synthesized into RTL, making it compatible with standard semiconductor manufacturing processes at foundries like TSMC, GlobalFoundries, and potentially Intel Foundry Services.12 This approach significantly lowers the barrier to adoption for semiconductor companies compared to analog or mixed-signal neuromorphic designs, reducing manufacturing risk and potentially cost.
The core operational principle of Akida is event-based processing. It analyzes sensor data at the point of acquisition, processing only essential information triggered by changes or "events" – often represented as "spikes".1 By leveraging the inherent sparsity in real-world data (i.e., the fact that often, not much changes from one moment to the next), Akida aims to dramatically reduce the number of computations required compared to conventional AI accelerators that process entire frames or data blocks continuously.5 This principle is the foundation for Akida's primary claimed benefit: ultra-low power consumption.1 Akida utilizes Spiking Neural Networks (SNNs), which process information encoded in the timing of these spikes, mirroring biological neural processing more closely than traditional Artificial Neural Networks (ANNs). A key part of BrainChip's offering is the capability within its software tools to convert conventional ANNs, such as Convolutional Neural Networks (CNNs), into SNNs that can run efficiently on the Akida hardware.17
2.2. Key Differentiators: Ultra-Low Power, On-Chip Learning, and Efficiency
BrainChip highlights three key differentiators for its Akida technology: ultra-low power consumption, unique on-chip learning capabilities, and high processing efficiency.
The claim of ultra-low power consumption is central to Akida's value proposition.1 By processing only sparse, event-based data, the architecture is designed to consume significantly less energy than traditional AI accelerators. Specific power figures cited include approximately 30 mW for the core technology 21, around 1 watt for an M.2 module incorporating the AKD1000 chip 1, and sub-milliwatt operation for the Akida Pico IP core designed for extremely constrained devices.11 One benchmark comparison suggested Akida processed the ImageNet dataset using 300 milliwatts versus 300 watts for a GPU, although such comparisons require careful contextualization.20 Another benchmark against Intel's Loihi 2 showed Akida 1000 consuming 1W compared to Loihi 2's 2.5W on a cybersecurity task, despite Akida being on an older process node.24 This low-power characteristic is positioned as enabling AI functionality in edge devices where energy budgets are severely limited, such as battery-powered sensors, wearables, and various IoT applications.11
A second major differentiator is on-chip learning.1 Akida is designed to allow AI models to learn and adapt after deployment, directly on the device, without requiring connection to the cloud for retraining.5 This capability is enabled by integrating learning rules, reportedly inspired by Spike-Timing-Dependent Plasticity (STDP), directly into the hardware.10 The potential benefits are significant for edge applications:
- Reduced Latency: Decisions and adaptations happen locally, avoiding cloud communication delays.1
- Enhanced Privacy and Security: Sensitive data can remain on the device, reducing exposure.1
- Lower Operational Costs: Eliminates the need for potentially expensive cloud-based retraining cycles.5
- Personalization and Adaptability: Devices can customize themselves to specific user needs or changing environments over time.12
- Future-Proofing: Devices can potentially learn new patterns or classifications in the field.12
Finally, BrainChip emphasizes efficiency and performance. The Akida architecture aims to minimize data movement – a primary source of power consumption in conventional AI systems – by placing entire neural networks onto the hardware fabric, reducing or eliminating the need to constantly swap weights and activations in and out of external memory (DRAM).7 The company claims this results in high throughput and precision with unparalleled energy efficiency across various data modalities, including vision, audio, and other sensor data.1
2.3. Akida Generations (1.0 vs. 2.0) and Product Implementations
The Akida architecture is designed to be scalable, configurable from 2 up to 256 interconnected nodes, allowing customization for different performance and power requirements.5 Each node comprises four Neural Processing Engines (NPEs), which can be configured as convolutional or fully connected layers, supported by local SRAM.12
The Akida 1.0 platform is based on the initial architecture, embodied in the AKD1000 chip (featuring a claimed 1.2 million neurons and 10 billion synapses 12) released in 2022 21, and the subsequent AKD1500. Akida 1.0 technology is available in several forms:
- IP License: For integration into third-party SoCs.1
- Chips and Development Kits: AKD1000 chips and development kits based on platforms like Raspberry Pi or x86 systems became available for evaluation and development.1
- Modules and Boards: Mini PCIe boards 15 and M.2 modules (starting at $249) were released to facilitate integration into edge devices.1
- Edge AI Box: A standalone enclosure integrating Akida for easier deployment.1 BrainChip received first silicon of the AKD1500, presumably an iteration within the 1.0 generation, from GlobalFoundries.15 However, a limitation noted for Akida 1.0 devices (AKD1000/1500) is their support for only a restricted set of Akida 1.0 layer types.21
- Higher Precision: Support for 8-bit weights and activations, compared to 1, 2, and 4-bit in Akida 1.0, potentially improving accuracy for complex models.
- Advanced Model Support: Improved acceleration for Vision Transformers (ViTs) and introduction of Temporal Event-based Neural Nets (TENNs) for efficient time-series processing.
- Enhanced Features: Multi-pass processing to run larger networks on smaller hardware implementations, configurable local scratchpads for memory optimization, and support for long-range skip connections (e.g., for ResNet-like architectures).
- Akida Pico: An ultra-low-power IP core variant targeting sub-milliwatt operation for battery-powered devices.11
However, there appears to be a delay in the hardware realization of Akida 2.0. While the platform was announced as available for engagement in October 2023 15, and general availability was previously targeted for Q3 2023 16, the AKD2000 hardware (presumably the silicon implementation of Gen 2) was noted as not yet released or available for purchase as of late 2024/early 2025.21 Although the MetaTF SDK has been updated to support Akida 2.0 features 21 and the company is engaging with lead adopters 16, this lag between the platform/IP availability and the corresponding purchasable hardware represents an execution risk. Delays in delivering Akida 2.0 silicon could create an opportunity for competitors to solidify their positions in the market for advanced edge AI processing.
2.4. Software and Development Ecosystem (MetaTF, TENNs, ViT Support)
A crucial element for the adoption of any novel hardware architecture is a robust and user-friendly software development environment. BrainChip addresses this through its MetaTF Software Development Kit (SDK).6 MetaTF is designed to integrate with standard AI workflows, specifically leveraging TensorFlow and Keras.1 This approach aims to lower the adoption barrier for the large community of developers already familiar with these tools, rather than requiring them to learn entirely new languages or frameworks specific to neuromorphic computing.19
MetaTF includes key components to bridge the gap between conventional ANN development and Akida's SNN hardware 21:
- quantizeml: A tool for quantizing models (reducing precision to 1, 2, 4, or 8 bits), retraining, and calibrating them for optimal performance on Akida.
- CNN2SNN: A package providing functions to convert these quantized ANNs into event-based SNNs that can be deployed on the Akida processor. BrainChip also provides a model zoo containing pre-built Akida-compatible models to accelerate development.21 The effectiveness and ease-of-use of MetaTF in handling model conversion, quantization, deployment, and managing the unique on-chip learning features are critical factors for developer adoption and, consequently, commercial success.
Similarly, Akida 2.0 includes enhanced support for Vision Transformers (ViTs).16 ViTs have become highly influential in computer vision, and the ability to accelerate them efficiently on low-power edge devices is a significant capability. BrainChip aims to leverage Akida's architecture to run these complex models effectively at the edge.16
To further bolster its ecosystem, BrainChip has pursued partnerships with key players in the AI development space, such as integrating with the Edge Impulse platform for ML development and deployment 15 and joining the Arm AI Partner Program.15 These collaborations aim to make Akida technology accessible and usable within broader development environments.
3. Market Landscape and Opportunity
BrainChip targets two overlapping but distinct market segments: the specific Neuromorphic Computing market and the broader Edge AI market.
3.1. The Neuromorphic Computing Market: Size, Growth Projections, and Trends
The neuromorphic computing market encompasses hardware and software systems designed based on principles of biological neural structures.10 It represents a paradigm shift from conventional computing, promising significant gains in energy efficiency and potentially new computational capabilities.
However, assessing the size and growth of this market is challenging due to its nascent stage and varying definitions used by market research firms. Forecasts exhibit significant discrepancies, highlighting the uncertainty surrounding this specific segment.
Table 1: Neuromorphic Market Forecast Comparison
| Source | Base Year | Base Value (USD) | Forecast Year | Forecast Value (USD Bn) | CAGR (%) | Notes |
Precedence Research 25 | 2024 | $6.90 Bn | 2034 | $47.31 | 21.23% (25-34) | Hardware 80% share (2024) |
GlobeNewswire (1) 34 | 2024 | $28.5 Million | 2030 | $1.32 | 89.7% (24-30) | Based on IoT Analytics / ResearchAndMarkets? |
Allied Market Research 35 | 2020 | $26.32 Million | 2030 | $8.58 | 77.00% (Hardware) | |
MarketsandMarkets 36 | 2024 | $28.5 Million | 2030 | $1.33 | 89.7% (24-30) | Aligned with GlobeNewswire (1) |
Dimension Market Research 37 | 2024 | $6.7 Bn | 2033 | $55.6 | 26.4% (24-33) | Aligned with Precedence Research scale |
As Table 1 illustrates, 2024 market size estimates range from a niche $28.5 million 34 to a substantial $6.9 billion.25 Projections for 2030-2034 diverge even more drastically, from $1.3 billion 34 to over $55 billion.37 While most forecasts predict strong growth (CAGRs from 21% to nearly 90%), the vast range makes it difficult to pinpoint the true current market size or reliable future value based solely on the "neuromorphic" label.
Key drivers identified across reports include the increasing demand for AI and Machine Learning capabilities, the need for more power-efficient computing alternatives to traditional methods, requirements for real-time data processing, and the growth of IoT and edge devices.25 Hardware (chips) currently dominates the market revenue, though software is expected to grow rapidly.25 Edge deployment is the primary model targeted.25 Geographically, North America holds the largest share, driven by major tech players and investments, while the Asia Pacific region is projected to exhibit the fastest growth.25 Dominant applications include image and video processing/computer vision, with data processing, signal processing, and sensor fusion also being significant.25 Key end-use verticals mirror those targeted by BrainChip: consumer electronics, automotive, industrial, healthcare, IT/telecom, and aerospace/defense.25
The significant ambiguity in market sizing suggests that investors should be cautious about relying solely on "neuromorphic computing" market forecasts to gauge BrainChip's opportunity. The company's success is more likely to be determined by its ability to capture share within the larger, better-defined Edge AI market, using its neuromorphic technology as a key differentiator.
3.2. The Edge AI Market: Size, Growth Projections, and Key Drivers
The Edge AI market involves deploying and running AI algorithms directly on end-user devices or local edge servers, rather than relying on centralized cloud data centers.19 This approach offers compelling advantages for many applications, including significantly reduced latency for real-time responses, enhanced data privacy and security by keeping data local, reduced bandwidth consumption and cost, and improved reliability in environments with intermittent connectivity.1
This market is substantially larger and more clearly defined than the niche neuromorphic segment, representing a significant opportunity for technologies like Akida. Market forecasts consistently point to a large and rapidly growing sector.
Table 2: Edge AI Market Forecast Comparison
| Source | Base Year | Base Value (USD Bn) | Forecast Year | Forecast Value (USD Bn) | CAGR (%) | Notes |
ResearchAndMarkets 38 | 2025 | $53.54 | 2030 | $81.99 | 8.84% (25-30) | |
Precedence Research 40* | 2025 | $10.13 | 2034 | $113.71 | 30.83% (25-34) | *Focuses on Edge AI Accelerators |
Roots Analysis 39 | 2024 | $24.05 | 2035 | $356.84 | 27.79% (24-35) | Hardware dominant component |
Grand View Research 41 | 2025 | $24.90 | 2030 | $66.47 | 21.7% (25-30) | Hardware 52.8% share (2024) |
STL Partners 42 | 2024 | ~$108 (Implied) | 2030 | $157 | ~6.4% (Implied) | TAM estimate; Computer Vision 50% share (2030) |
Table 2 shows that the Edge AI market was already valued in the tens of billions USD in 2024/2025, with forecasts projecting significant growth, reaching anywhere from approximately $66 billion to over $350 billion by 2030-2035.38 Projected CAGRs generally range from around 9% to over 30%, indicating robust expansion.38
The primary drivers fueling this growth are 36:
- The exponential proliferation of Internet of Things (IoT) devices across consumer, industrial, and enterprise sectors.
- The increasing demand for real-time data processing and decision-making in applications like autonomous vehicles, robotics, and industrial automation (Industry 4.0).
- The rollout of 5G networks, enabling higher bandwidth and lower latency connectivity for edge devices.
- Growing requirements for data privacy and security, favoring on-device processing.
- Advancements in AI algorithms and the need to deploy them efficiently outside of data centers.
BrainChip's core value proposition – ultra-low power consumption, low latency processing, and on-device learning – directly addresses key challenges and requirements within this large and expanding Edge AI market.1 This strong alignment validates the strategic importance of the market opportunity for BrainChip. The primary challenge lies in execution: successfully competing against established players and translating technological potential into significant market share and revenue within this dynamic landscape.
3.3. Target Applications and Verticals
The specific features of the Akida processor – its event-based nature, low power draw, and on-chip learning – make it potentially suitable for a wide spectrum of edge AI applications where these characteristics are advantageous. BrainChip highlights numerous use cases across various industry verticals 1:
- Audio/Voice: Keyword spotting ("Hey Mercedes", "OK Google") 2, voice wake detection, voice command recognition, noise reduction/audio enhancement (e.g., for hearables), speaker identification.16
- Vision: Visual wake words (person detection) 2, face detection/recognition 7, gesture recognition 13, object detection/classification 7, image segmentation 7, video object tracking.16
- Sensor/IoT: Anomaly detection (e.g., vibration analysis for predictive maintenance) 16, sensor fusion 16, presence detection 16, smart transducer applications.8
- Specialized: Radar/EW signal processing 2, cybersecurity threat detection 15, medical diagnostics/imaging support 5, space applications.3
- Automotive: In-cabin experiences (voice control, driver monitoring), potentially ADAS components (though likely requiring significant further development/validation), powertrain optimization.1
- Consumer Electronics: Smartphones, smart speakers, wearables, smart home devices, hearables.1
- Industrial IoT: Factory automation, robotics, predictive maintenance, smart sensors, asset tracking.1
- Aerospace & Defense: Radar systems, satellite processing, drones, cybersecurity, secure communications.2
- Healthcare & Medical: Medical diagnostic tools, vital sign monitoring, personalized health devices.16
- IT, Telecom & Cybersecurity: Network edge devices, threat detection systems.24
FiveBucks
Regular
Part 2
4. Competitive Analysis
BrainChip faces competition on two main fronts: directly from other companies developing neuromorphic processors, and more broadly from the wide array of established and emerging players in the Edge AI hardware market.
4.1. Neuromorphic Competitors
The field of neuromorphic computing, while still emerging commercially, includes several notable players, primarily large semiconductor companies investing in research and development, alongside specialized startups.
4.2. Edge AI Hardware Competitors
Beyond the specific neuromorphic niche, BrainChip competes against a diverse range of established hardware solutions for edge AI workloads. Customers evaluating edge AI solutions consider various factors, including performance, power consumption, cost, ease of development, software support, and existing ecosystem familiarity.
Key categories and players include:
4.3. BrainChip's Competitive Positioning: Strengths, Weaknesses, Opportunities, Threats (SWOT)
A SWOT analysis summarizes BrainChip's competitive standing:
5. Business Model, Commercialization, and Partnerships
5.1. Intellectual Property (IP) Licensing Strategy
BrainChip's core business strategy revolves around licensing its Akida neuromorphic processor IP to semiconductor companies and OEMs. Instead of primarily manufacturing and selling its own chips at scale, BrainChip provides the design blueprints and associated software for partners to integrate into their own SoCs. This fabless IP licensing model is common in the semiconductor industry, employed successfully by companies like Arm and Imagination Technologies.
The anticipated revenue structure for BrainChip includes several components :
Upfront License Fees: Paid by the licensee for the right to access and use the Akida IP in their designs.
However, this model also presents significant challenges, particularly regarding the time lag between signing a licensing deal and realizing substantial royalty revenue. The licensee must complete their own complex SoC design cycle, tape-out, silicon validation, software integration, product launch, and finally achieve market traction and volume shipments before royalties flow back to BrainChip. This extended timeline creates uncertainty and delays visibility into revenue ramp-up. The lack of significant reported royalty revenue from major early licensees like Renesas and MegaChips, several years after agreements were announced in late 2020 and 2021 respectively , is a critical concern. While Renesas only taped out its Akida-based chip in late 2022 , the persistent absence of material royalties raises questions about the actual time-to-market for partners' products, their initial sales volumes, or whether agreed-upon royalty thresholds have been met. Public financial reports from Renesas and MegaChips do not provide specific details on royalty payments made to BrainChip. This lack of tangible royalty income significantly impacts the ability to forecast BrainChip's path to profitability and validates concerns about the practical timeline of its commercialization strategy.
5.2. Key Partnerships and Licensing Agreements
BrainChip has announced numerous partnerships and agreements across various categories: IP licensing, technology collaboration, ecosystem enablement, and customer engagements.
5.3. Commercialization Progress and Customer Pipeline Assessment
BrainChip's commercialization journey began in earnest with the availability of the AKD1000 chip and associated development platforms in late 2021 and early 2022. This followed the crucial IP licensing agreements with Renesas and MegaChips in late 2020 and 2021. The Mercedes-Benz Vision EQXX showcase in 2022 provided significant visibility. The focus shifted towards the Akida 2.0 platform announcement and initial availability in 2023 , followed by a series of announced engagements primarily in the space and defense sectors in late 2024 and early 2025.
Despite these milestones and an expanding partner list, tangible commercial success, measured by significant revenue generation, has remained elusive. Company leadership acknowledges the slower-than-anticipated progress but maintains confidence in the underlying technology and the growing customer pipeline. Management highlighted engagement with "dozens of new prospects" at CES 2024 and stated that the focus for 2025 is squarely on "execution and delivering commercial success," supported by intensified operational rigor.
However, a significant gap persists between the announced partnerships, claimed pipeline strength, and the actual financial results reported. Converting initial interest, evaluations (through Early Access Programs or dev kit purchases), and even initial license agreements into substantial, recurring royalty revenue remains the critical challenge. The path from technology evaluation to high-volume product deployment by licensees is long and uncertain, and BrainChip has yet to demonstrate successful navigation of this full cycle with its key partners.
https://www.eetimes.com/podcasts/brainchips-ip-for-targeting-ai-applications-at-the-edge/
4. Competitive Analysis
BrainChip faces competition on two main fronts: directly from other companies developing neuromorphic processors, and more broadly from the wide array of established and emerging players in the Edge AI hardware market.
4.1. Neuromorphic Competitors
The field of neuromorphic computing, while still emerging commercially, includes several notable players, primarily large semiconductor companies investing in research and development, alongside specialized startups.
- Intel (Loihi / Loihi 2):Intel is perhaps the most prominent competitor with its Loihi research chips. Loihi 2, built on a more advanced process node than Akida 1000, features on-chip learning capabilities and event-driven processing. Intel has demonstrated large-scale systems like Hala Point (1.15 billion neurons) and collaborates with research institutions like Sandia National Labs. While historically positioned as a research platform , Intel's continued investment and integration into initiatives like its Foundry Services (IFS) ecosystem suggest potential future commercial ambitions. Direct comparisons based on a cybersecurity benchmark study showed Akida 1000 achieving higher accuracy (98.4% vs 90.2%) and lower power consumption (1W vs 2.5W) than Loihi 2 on that specific task, though Loihi 2 showed advantages in batch processing throughput. BrainChip also positions Akida as easier for engineers to use compared to Loihi's perceived focus on neuroscientists.
- IBM (TrueNorth):IBM was an early pioneer with its TrueNorth chip. However, public information suggests TrueNorth development has been less active commercially in recent years compared to Intel's Loihi efforts.
- SpiNNaker:Developed by the University of Manchester, SpiNNaker is a large-scale platform primarily focused on academic research and simulating large neural networks. It is not positioned as a direct commercial competitor for edge IP licensing.
- SynSense:A Swiss startup developing neuromorphic processors, mentioned as having launched development kits for ultra-low-power applications.
- Others: Various research institutions and startups are exploring neuromorphic approaches, but few have reached the commercialization stage claimed by BrainChip.
4.2. Edge AI Hardware Competitors
Beyond the specific neuromorphic niche, BrainChip competes against a diverse range of established hardware solutions for edge AI workloads. Customers evaluating edge AI solutions consider various factors, including performance, power consumption, cost, ease of development, software support, and existing ecosystem familiarity.
Key categories and players include:
- GPUs (Graphics Processing Units):NVIDIA dominates the high-performance edge AI space with its Jetson platform (e.g., Nano, Xavier NX, Orin series). Jetsons offer strong performance, particularly for computer vision, backed by NVIDIA's mature CUDA software ecosystem and TensorRT optimization tools. Their main drawback is typically higher power consumption compared to more specialized solutions.
- ASICs (Application-Specific Integrated Circuits) & AI Accelerators:Companies like Google (Coral Edge TPU) , Apple (Neural Engine in A-series and M-series chips), Qualcomm (AI Engine in Snapdragon platforms), and numerous startups offer chips specifically designed for efficient AI inference. These can provide excellent performance-per-watt for targeted tasks but are generally less flexible than GPUs or FPGAs. Google's Coral TPU, for instance, has faced criticism for limitations like small batch sizes, lower precision, and initial reliance on cloud compilation.
- FPGAs (Field-Programmable Gate Arrays):Providers like AMD (Xilinx) and Intel (Altera) offer FPGAs (e.g., Zynq, Alveo, Arria, Stratix). FPGAs provide hardware reconfigurability, allowing optimization for specific AI models and potentially good energy efficiency. However, FPGA development can be more complex and time-consuming than software-based approaches on CPUs or GPUs.
- MCUs (Microcontroller Units) with AI Capabilities:Increasingly, traditional MCU vendors like STMicroelectronics, NXP, Renesas, and Microchip are integrating lightweight AI acceleration features (e.g., Arm Ethos NPUs) into their low-power, low-cost microcontrollers. These are suitable for simpler "TinyML" tasks like basic keyword spotting or sensor analysis but lack the performance for complex models. BrainChip's own benchmarks suggest Akida offers significant advantages in latency and energy efficiency over standard MCUs for tasks like visual wake words and anomaly detection.
4.3. BrainChip's Competitive Positioning: Strengths, Weaknesses, Opportunities, Threats (SWOT)
A SWOT analysis summarizes BrainChip's competitive standing:
- Strengths:
- Unique Technology:Event-based, neuromorphic architecture offering potential for disruptive ultra-low power consumption.
- On-Chip Learning:Differentiated capability enabling local adaptation, personalization, and enhanced privacy.
- IP Licensing Model:Potential for high margins and scalability if widely adopted.
- Commercial Focus:Positioned as the first commercial provider of neuromorphic IP, with available development hardware.
- Growing Partner Ecosystem:Numerous partnerships across technology, integration, and academia.
- Validation:Engagements with high-profile names like Mercedes (concept), Renesas, MegaChips, Frontgrade, AFRL provide technical validation.
- Unique Technology:Event-based, neuromorphic architecture offering potential for disruptive ultra-low power consumption.
- Weaknesses:
- Minimal Revenue:Very low revenue generation to date, lack of significant royalty income.
- High Cash Burn:Consistent operating losses necessitate ongoing external funding.
- Execution Risk:Potential delays in Akida 2.0 hardware rollout impacting competitiveness.
- Unproven Commercial Scalability:The IP model's ability to generate substantial royalties remains unproven.
- Resource Constraints:Smaller size and financial resources compared to major semiconductor competitors (Intel, Nvidia, Qualcomm).
- Adoption Friction:Potential challenges in convincing developers to adopt a novel architecture and SNNs, despite tools like MetaTF.
- Minimal Revenue:Very low revenue generation to date, lack of significant royalty income.
- Opportunities:
- Large Growing Markets:Positioned to benefit from rapid growth in Edge AI and potentially Neuromorphic Computing.
- Demand for Low Power AI:Increasing need for energy-efficient AI solutions in battery-powered and constrained devices.
- Leverage Partnerships:Utilize partners for market access, manufacturing, and ecosystem building.
- US Redomiciling:Potential to attract a wider pool of US technology investors and simplify reporting.
- Niche Market Expansion:Capitalize on demonstrated interest in high-value, low-SWaP niches like space, defense, and potentially medical.
- Threats:
- Intense Competition:Strong incumbents (Nvidia, Google, Qualcomm) and numerous startups in the Edge AI space. Intel's potential commercialization of Loihi.
- Slow Market Adoption:Neuromorphic computing and SNNs may face slower adoption than anticipated due to ecosystem immaturity or perceived complexity.
- Technological Obsolescence: Rapid pace of AI innovation could lead to competing technologies achieving similar power efficiency or performance gains through different means.
- Partner Failure:Risk that key licensees (e.g., Renesas, MegaChips) fail to successfully commercialize Akida-based products or achieve significant volumes.
- Funding Dependency & Dilution:Continued reliance on funding arrangements (like LDA Capital) leads to shareholder dilution and financial pressure.
- Execution Failures:Delays in product roadmap, failure to secure major commercial wins, challenges with US listing process.
| Feature | BrainChip Akida (Gen 1/2) | Intel Loihi 2 | Nvidia Jetson (e.g., Orin Nano) | Google Coral Dev Board Mini |
Architecture Type | Digital Neuromorphic (SNN/Event-Based) | Digital Neuromorphic (SNN/Event-Based) | GPU (ANN/Conventional) | ASIC (Edge TPU - ANN/Conventional) |
Power Profile | Ultra-Low (<1mW - ~1W typical) | Very Low (~2.5W reported) | Moderate-High (e.g., 7W-15W TDP) | Low (~2W reported) |
On-Chip Learning | Yes (Key Feature) | Yes | No (Requires offline training) | No (Requires offline training) |
Ecosystem/Software | MetaTF (TensorFlow/Keras based), Edge Impulse | Lava Framework, Loihi SDK | Mature (CUDA, TensorRT, JetPack SDK) | TensorFlow Lite, AutoML Edge |
Target Applications | Low-power sensing, KWS, VWW, anomaly det., vision | Research, potentially similar edge tasks | Robotics, vision, high-performance edge AI | Edge inference, prototyping |
Commercial Status | IP License, Dev Kits/Boards/Box Available | Primarily Research, IFS ecosystem partner | Widely Available (Modules/Dev Kits) | Widely Available (Modules/Dev Kits) |
5. Business Model, Commercialization, and Partnerships
5.1. Intellectual Property (IP) Licensing Strategy
BrainChip's core business strategy revolves around licensing its Akida neuromorphic processor IP to semiconductor companies and OEMs. Instead of primarily manufacturing and selling its own chips at scale, BrainChip provides the design blueprints and associated software for partners to integrate into their own SoCs. This fabless IP licensing model is common in the semiconductor industry, employed successfully by companies like Arm and Imagination Technologies.
The anticipated revenue structure for BrainChip includes several components :
Upfront License Fees: Paid by the licensee for the right to access and use the Akida IP in their designs.
- Milestone Payments: Potential payments tied to specific stages of the licensee's product development cycle.
- Recurring Royalties:Payments based on the volume of units sold by the licensee that incorporate the Akida IP. Royalties are typically calculated as a percentage of the net selling price of the licensee's chip or module, often kicking in after a certain volume threshold is reached.
- Support and Maintenance Fees:Charges for ongoing technical support, software updates, and implementation assistance provided by BrainChip.
However, this model also presents significant challenges, particularly regarding the time lag between signing a licensing deal and realizing substantial royalty revenue. The licensee must complete their own complex SoC design cycle, tape-out, silicon validation, software integration, product launch, and finally achieve market traction and volume shipments before royalties flow back to BrainChip. This extended timeline creates uncertainty and delays visibility into revenue ramp-up. The lack of significant reported royalty revenue from major early licensees like Renesas and MegaChips, several years after agreements were announced in late 2020 and 2021 respectively , is a critical concern. While Renesas only taped out its Akida-based chip in late 2022 , the persistent absence of material royalties raises questions about the actual time-to-market for partners' products, their initial sales volumes, or whether agreed-upon royalty thresholds have been met. Public financial reports from Renesas and MegaChips do not provide specific details on royalty payments made to BrainChip. This lack of tangible royalty income significantly impacts the ability to forecast BrainChip's path to profitability and validates concerns about the practical timeline of its commercialization strategy.
5.2. Key Partnerships and Licensing Agreements
BrainChip has announced numerous partnerships and agreements across various categories: IP licensing, technology collaboration, ecosystem enablement, and customer engagements.
- Major IP Licensees:
- Renesas Electronics:Signed a single-use, royalty-bearing, worldwide IP design license for Akida 1.0 in December 2020. Renesas targeted the IP for its IoT and Infrastructure Business Unit. A device incorporating the technology was reportedly taped out on a 22nm CMOS process in December 2022. Renesas management indicated that market reception would determine further internal development versus reliance on third-party IP. No significant royalty revenue has been reported by BrainChip from this agreement to date.
- MegaChips Corporation:Announced a licensing agreement in November 2021 for Akida IP to be used in their next-generation edge AI solutions. Public information on the status of MegaChips' product development or any resulting royalties is limited.
- Frontgrade Gaisler:Signed a commercial license agreement in December 2024 for Akida 1.0 IP for use in radiation-hardened microprocessors targeting space applications. This followed a successful evaluation phase involving the European Space Agency (ESA). This represents a significant validation in a demanding, albeit lower-volume, market.
- High-Profile Collaborations & Engagements:
- Mercedes-Benz: Utilized Akida hardware and software in the Vision EQXX concept carunveiled in early 2022. The application focused on low-power keyword spotting ("Hey Mercedes"), with Mercedes claiming a 5-10x efficiency improvement over conventional voice control. While Mercedes acknowledged the potential of neuromorphic computing for future vehicles, this collaboration was explicitly limited to a concept demonstration. This engagement provided valuable technical validation and significant positive publicity , demonstrating Akida's capabilities in a relevant automotive context. However, it should not be interpreted as a design win for a volume production vehicle, which would involve much longer design cycles and qualification processes. Confirmation of integration into production models is required before factoring in substantial automotive revenue.
- Defense & Aerospace Sector:BrainChip has recently highlighted several engagements in this area:
- Information Systems Laboratories (ISL):Partnership formalized in early 2025 for AI-based radar research solutions, leveraging ISL's expertise and Akida's low Size, Weight, Power, and Cost (SWaP-C) advantages for military and aerospace platforms. ISL was an early access program member.
- US Air Force Research Laboratory (AFRL):Awarded a $1.8M contract (via an unnamed major defense contractor as subcontractor) in late 2024/early 2025 for developing radar processing techniques (micro-Doppler analysis) using Akida.
- Bascom Hunter:Secured a $100k contract in late 2024 for the sale and support of AKD1500 chips for evaluation in potential commercial products.
NASA Ames Research Center:Procured an Akida Early Access Evaluation Kit in late 2020 to assess the technology for spaceflight applications.
- The recent focus on securing contracts and licenses within the space and defense sectors (Frontgrade, ISL, AFRL, Bascom Hunter) provides strong validation for Akida's performance in demanding, low-SWaP environments. However, these markets typically involve lower production volumes compared to consumer electronics or mainstream automotive segments. While potentially providing valuable non-recurring engineering (NRE) or license fee revenue in the near term, these engagements may not be sufficient on their own to drive the high-volume royalty streams necessary for the IP model's long-term success. It remains unclear whether this focus is a strategic choice to build credibility or reflects challenges in penetrating higher-volume commercial markets.
- Technology & Ecosystem Partners: BrainChip has cultivated a broad network of partners to support its technology and ecosystem :
- CPU/SoC Platforms:SiFive (RISC-V) , Arm (Partner Program member, Cortex-M85 integration) , Andes Technology (RISC-V).
- Manufacturing & Foundry:Socionext (initial AKD1000 manufacturing partner) , GlobalFoundries (AKD1500 tape-out on 22nm FD-SOI) , Intel Foundry Services (IFS Accelerator IP Alliance member).
- Software & Tools:Edge Impulse (ML development platform integration).
- System Integration & Applications:Microchip (MPU integration demo) , CVEDIA (video analytics) , NVISO (human behavioral analytics) , VVDN (developing Akida Edge AI Box) , Tata Elxsi (solutions provider) , Quantum Ventura (cybersecurity) , Unigen (Edge AI Server) , MYWAI (AI solutions) , Eastronics & SalesLink (sales partners in EMEA).
- Academic:University AI Accelerator Program involving institutions like Cornell Tech.
| Partner | Area/Application | Agreement Type | Announced Date | Known Status/Outcome |
Renesas Electronics | Edge AI SoCs (IoT, Infrastructure) | IP License (Akida 1.0) | Dec 2020 | Chip taped out Dec 2022. No reported royalties yet. Market adoption pending. |
MegaChips Corp. | Edge AI Solutions | IP License (Akida) | Nov 2021 | Product status/royalties unclear. |
Mercedes-Benz | Automotive (Keyword Spotting) | Collaboration (Concept Car) | Jan 2022 | Successful tech demo in Vision EQXX. No production vehicle win announced. |
Frontgrade Gaisler | Space (Radiation-Hardened Processors) | IP License (Akida 1.0) | Dec 2024 | Commercial license signed after successful ESA evaluation. Products likely in development. |
ISL | Defense/Aerospace (Radar Research) | Partnership | Mar 2025 | Joint promotion/services for Akida-based radar solutions. Followed EAP membership. |
AFRL (via Subcontractor) | Defense (Radar Processing) | Contract ($1.8M) | Dec 2024/Jan 2025 | Developing micro-Doppler analysis algorithms using Akida. |
Bascom Hunter | Defense/Commercial Product Evaluation | Contract ($100k) | Dec 2024 | Sale/support of AKD1500 chips for evaluation. |
NASA Ames | Spaceflight Applications | Evaluation Kit Purchase | Dec 2020 | Evaluating Akida technology. |
Socionext | Manufacturing Partner | Agreement | Jun 2019/Aug 2021 | Manufactured initial AKD1000 production chips. |
GlobalFoundries | Manufacturing Partner | Agreement | Jan 2023 | Taped out AKD1500 chip on 22nm FD-SOI process. Received first silicon Aug 2023. |
Intel Foundry Services | Foundry Ecosystem | Alliance Member | Dec 2022 | BrainChip joined IFS Accelerator IP Alliance. |
Edge Impulse | ML Development Platform | Partnership/Integration | Jan 2023/Aug 2022 | Akida platform supported on Edge Impulse for easier deployment. |
Arm | CPU Ecosystem | Partner Program Member | May 2022 | Integration with Cortex-M85 demonstrated. |
SiFive | CPU Ecosystem (RISC-V) | Partnership | Apr 2022 | Collaboration on AI/ML at the edge using RISC-V. |
5.3. Commercialization Progress and Customer Pipeline Assessment
BrainChip's commercialization journey began in earnest with the availability of the AKD1000 chip and associated development platforms in late 2021 and early 2022. This followed the crucial IP licensing agreements with Renesas and MegaChips in late 2020 and 2021. The Mercedes-Benz Vision EQXX showcase in 2022 provided significant visibility. The focus shifted towards the Akida 2.0 platform announcement and initial availability in 2023 , followed by a series of announced engagements primarily in the space and defense sectors in late 2024 and early 2025.
Despite these milestones and an expanding partner list, tangible commercial success, measured by significant revenue generation, has remained elusive. Company leadership acknowledges the slower-than-anticipated progress but maintains confidence in the underlying technology and the growing customer pipeline. Management highlighted engagement with "dozens of new prospects" at CES 2024 and stated that the focus for 2025 is squarely on "execution and delivering commercial success," supported by intensified operational rigor.
However, a significant gap persists between the announced partnerships, claimed pipeline strength, and the actual financial results reported. Converting initial interest, evaluations (through Early Access Programs or dev kit purchases), and even initial license agreements into substantial, recurring royalty revenue remains the critical challenge. The path from technology evaluation to high-volume product deployment by licensees is long and uncertain, and BrainChip has yet to demonstrate successful navigation of this full cycle with its key partners.
https://www.eetimes.com/podcasts/brainchips-ip-for-targeting-ai-applications-at-the-edge/
Last edited:
FiveBucks
Regular
Part 3
6. Financial Analysis and Health
BrainChip's financial position reflects its status as a pre-revenue or early-revenue technology company investing heavily in R&D and commercialization efforts while generating minimal income.
6.1. Historical Financial Performance (Revenue, Expenses, Net Loss)
BrainChip's reported revenues over the past three fiscal years (ending December 31) have been minimal and volatile, primarily reflecting upfront license fees or development kit sales rather than recurring royalties.
6.2. Cash Flow, Burn Rate, and Funding
The company's operating activities consistently consume cash.
6.3. Balance Sheet Analysis and Financial Health
BrainChip's balance sheet as of December 31, 2024, reflects its early-stage nature.
7. Stock Performance, Analyst Coverage, and Sentiment
7.1. Historical Stock Performance (ASX:BRN)
BrainChip's stock (ASX:BRN) has exhibited extreme volatility, characteristic of speculative technology stocks with significant future potential but limited current financial results.
The provided research materials do not contain specific consensus analyst ratings or price targets from major brokerage firms (e.g., AFR, Bloomberg, Reuters data was not included beyond general market info ). One independent research report included in the materials (Pitt Street Research, June 2024) assigned a speculative "fair valuation" of A$1.59 per share, based on industry M&A activity rather than traditional financial metrics, while acknowledging investor patience had been tested. Another source (Zacks Small-Cap Research, Nov 2022) provided analysis but did not specify a target price. InvestingPro data cited a "Fair Value" estimate range but this is typically model-driven rather than analyst consensus. The lack of broad, mainstream analyst coverage is typical for highly speculative, pre-revenue companies.
7.3. News Sentiment and Investor Communication
News flow is a critical driver of BrainChip's share price.
Investing in BrainChip involves significant risks inherent in early-stage technology companies operating in highly competitive markets.
BrainChip Holdings stands at a critical juncture. The company possesses innovative neuromorphic technology, Akida, with compelling theoretical advantages for the burgeoning edge AI market, particularly in ultra-low-power applications. Its event-based processing and on-chip learning capabilities differentiate it from conventional AI hardware. High-profile partnerships and engagements provide validation of the technology's potential in demanding sectors like automotive (concept), space, and defense.
However, the path to commercial success remains fraught with significant challenges and uncertainties. The transition from technology development and evaluation to generating substantial, recurring revenue through its IP licensing model has proven slower and more difficult than perhaps initially anticipated. The lack of material royalty revenue from key early licensees is a major concern, raising questions about the timelines and commercial viability of partners' Akida-based products. While recent contract wins in defense and space are positive developments, they are unlikely to provide the high volumes needed to achieve profitability in the near term.
The company's success over the next 12 months, and beyond, hinges critically on execution across several fronts:
BrainChip's market capitalization (currently around A$550M ) reflects a high degree of speculation regarding its future success rather than current financial performance. Over the next 12 months, its valuation is likely to be driven primarily by:
BrainChip Holdings presents a high-risk, potentially high-reward investment proposition. The Akida technology offers a genuinely innovative approach to AI processing at the edge, targeting critical needs for low power and on-device intelligence. The alignment with the large and rapidly growing Edge AI market is clear. However, the company faces formidable challenges in commercial execution, competition from established giants, and managing its financial resources.
Success is far from guaranteed and depends heavily on management's ability to convert technological promise and partnership potential into tangible, significant revenue streams in the relatively near future. The next 12 months are likely to be pivotal, with progress on Akida 2.0, securing major commercial design wins leading to royalties, and navigating the funding landscape being critical determinants of the company's trajectory and market valuation. Investors must weigh the substantial potential market opportunity against the significant execution risks and the company's current financial fragility. Predicting the market capitalization remains inherently uncertain and highly dependent on the successful achievement of key commercial and technological milestones.
Sources used in the report
6. Financial Analysis and Health
BrainChip's financial position reflects its status as a pre-revenue or early-revenue technology company investing heavily in R&D and commercialization efforts while generating minimal income.
6.1. Historical Financial Performance (Revenue, Expenses, Net Loss)
BrainChip's reported revenues over the past three fiscal years (ending December 31) have been minimal and volatile, primarily reflecting upfront license fees or development kit sales rather than recurring royalties.
- FY 2022:Revenue reported was approximately $5.1 million (or $7.5M per ), largely attributed to performance obligations related to a significant license agreement (likely Renesas or MegaChips) and sales of development kits.
- FY 2023:Revenue decreased dramatically to approximately $0.2 million. The company acknowledged it did not secure royalty-bearing IP sales agreements during the year.
- FY 2024:Revenue showed a slight increase to approximately $0.4 million (or $0.6M per ). The company explicitly stated it "did not deliver on its goal to achieve significant growth in license and product revenue".
- FY 2022:Total operating expenses were approximately $27.0 million.
- FY 2023:Total operating expenses increased to approximately $28.8 million. Key components included Selling & Marketing ($4.7M, up 50% YoY) and Share-based payments ($11.4M, up 24% YoY).
- FY 2024:Total operating expenses decreased by 17% to $23.9 million. Research & Development (R&D) expenses were approximately $7.7 million, a 9% increase from FY 2023, driven by employee costs offset by reduced grant revenue and third-party service costs following the redundancy of the Australian R&D team.
- FY 2022:Net loss after tax was $22.1 million.
- FY 2023:Net loss after tax widened to $28.9 million.
- FY 2024:Net loss after tax narrowed slightly to $24.4 million.
6.2. Cash Flow, Burn Rate, and Funding
The company's operating activities consistently consume cash.
- Quarterly Operating Cash Outflows:Recent quarterly reports indicate net operating cash outflows typically ranging from $3.4 million to $4.1 million per quarter. For the quarter ending Dec 31, 2024, net operating cash outflow was $4.1M, with minimal customer receipts ($0.05M) and payments to suppliers/employees of $4.3M. This suggests an annual operating cash burn rate in the vicinity of $15-17 million, excluding financing and investing activities.
- Cash Balance:As of December 31, 2024, the company reported a cash balance of approximately $20.0 million USD. This compares to $14.3 million USD at the end of 2023 and $23.2 million USD at the end of 2022. The increase during 2024 was due to financing activities.
- LDA Capital POA:This agreement allows BrainChip to issue shares to LDA Capital in exchange for cash, subject to certain conditions and pricing mechanisms. As of December 31, 2024, the total commitment amount under the amended POA was A$140 million, of which A$68 million in gross proceeds had been drawn since 2020. A further minimum drawdown of A$20 million is required by June 30, 2026. This facility provides access to capital but results in ongoing share issuance and dilution for existing shareholders. BrainChip issued 40 million shares under this facility in early 2025.
- Equity Capital Raise (July 2024):BrainChip raised A$25 million through a placement to institutional investors (A$20M), sale of existing shares from LDA (A$2M), and a Share Purchase Plan (SPP) for retail investors (A$3M), all at an issue price of A$0.193 per share.
- Option Exercises:The company also receives cash inflows from the exercise of employee stock options (e.g., $0.5M in Q4 2024).
6.3. Balance Sheet Analysis and Financial Health
BrainChip's balance sheet as of December 31, 2024, reflects its early-stage nature.
- Assets:Total assets were approximately $37.1 million USD. The vast majority consisted of current assets, primarily cash and cash equivalents ($32.2M, though this figure seems inconsistent with the $20M reported in the quarterly update - likely due to timing differences or reporting conventions). Non-current assets were minimal ($2.3M), mainly comprising property, plant & equipment and minor intangible assets. Capitalized R&D costs appear to be amortized or impaired rather than carried as significant intangible assets.
- Liabilities:Total liabilities were relatively low at $5.2 million USD. Current liabilities ($3.8M) included accounts payable and provisions. Long-term debt was minimal ($1.2M). The company highlights holding more cash than debt.
- Equity:Total shareholders' equity was $31.9 million USD. This primarily consisted of share capital ($269.9M) and reserves ($80.8M), offset by large accumulated losses/retained deficits (-$318.8M).
7. Stock Performance, Analyst Coverage, and Sentiment
7.1. Historical Stock Performance (ASX:BRN)
BrainChip's stock (ASX:BRN) has exhibited extreme volatility, characteristic of speculative technology stocks with significant future potential but limited current financial results.
- Price Range:Over the 52 weeks leading up to late 2024/early 2025, the stock traded in a wide range, for example, between A$0.155 and A$0.45. A research note from June 2024 cited a 52-week high/low of A$0.49 / A$0.15. The price was around A$0.20-A$0.27 in mid-to-late 2024/early 2025 according to various sources.
- Volatility:The stock price has experienced sharp rallies often driven by announcements (e.g., partnerships like Mercedes ) and significant declines following periods of limited news flow or disappointing financial results. Trading volume is substantial, averaging over 10 million shares daily in mid-2024.
- Market Capitalization:Reflecting the share price volatility and ongoing share issuance, the market capitalization has fluctuated significantly. It was cited as A$371M (A$405M fully diluted) in June 2024 , around A$437M in late 2024 , and approximately A$547M in April 2025. This places BrainChip among the smaller listed technology companies on the ASX.
The provided research materials do not contain specific consensus analyst ratings or price targets from major brokerage firms (e.g., AFR, Bloomberg, Reuters data was not included beyond general market info ). One independent research report included in the materials (Pitt Street Research, June 2024) assigned a speculative "fair valuation" of A$1.59 per share, based on industry M&A activity rather than traditional financial metrics, while acknowledging investor patience had been tested. Another source (Zacks Small-Cap Research, Nov 2022) provided analysis but did not specify a target price. InvestingPro data cited a "Fair Value" estimate range but this is typically model-driven rather than analyst consensus. The lack of broad, mainstream analyst coverage is typical for highly speculative, pre-revenue companies.
7.3. News Sentiment and Investor Communication
News flow is a critical driver of BrainChip's share price.
- Positive Sentiment Drivers:Announcements of partnerships (Mercedes, Renesas, Frontgrade, AFRL), technology milestones (Akida 2.0, Akida Pico), product availability (M.2 modules, Edge AI Box), patent awards, and positive benchmarking results tend to generate positive sentiment and share price appreciation. The proposed US redomiciling was presented as a potential positive catalyst.
- Negative Sentiment Drivers:Quarterly reports revealing continued losses and minimal revenue, delays in commercialization or product rollouts (e.g., Akida 2.0 hardware), capital raisings and associated dilution, and periods of limited substantive news can negatively impact sentiment and the share price. Concerns about high executive remuneration relative to performance have also been raised by shareholders, although the company defends its policies as necessary to attract talent.
- Investor Communication:BrainChip actively communicates through ASX announcements, press releases, participation in industry events (CES, Embedded World) , and a regular investor podcast series featuring CEO updates and addressing shareholder questions. This proactive communication aims to keep investors informed and manage expectations, though the share price remains highly sensitive to the substance (or lack thereof) in these updates.
Investing in BrainChip involves significant risks inherent in early-stage technology companies operating in highly competitive markets.
- Technological Risks:
- Performance Shortfalls: Akida technology may not deliver the promised performance or power efficiency across the full range of target applications compared to competing solutions.
- Development Delays:Delays in the development, validation, and rollout of crucial next-generation products like Akida 2.0 hardware could allow competitors to gain an advantage.
- SNN Complexity:Spiking Neural Networks are less understood and utilized than traditional ANNs, potentially hindering adoption and requiring significant developer effort despite tools like MetaTF.
- Scalability Challenges: Scaling the technology to handle increasingly complex AI models while maintaining efficiency may pose unforeseen challenges.
- Market and Commercialization Risks:
- Slow Market Adoption:The market for neuromorphic computing may develop slower than anticipated, or customers may prefer established AI architectures.
- Failure to Secure Key Design Wins:Inability to convert pipeline opportunities and partnerships into high-volume commercial license agreements with significant royalty potential.
- Partner Execution Failure:Key licensees (Renesas, MegaChips, etc.) may face delays or fail to successfully bring their Akida-based products to market or achieve significant sales volumes.
- Competition:Intense competition from large semiconductor companies (Intel, Nvidia, Qualcomm, AMD) and numerous AI hardware startups offering alternative edge AI solutions.
- Slow Market Adoption:The market for neuromorphic computing may develop slower than anticipated, or customers may prefer established AI architectures.
- Financial Risks:
- Ongoing Losses and Cash Burn:Continued operating losses require ongoing funding, depleting cash reserves.
- Funding Dependency:Reliance on external capital (e.g., LDA Capital facility, equity raises) makes the company vulnerable to market conditions and leads to shareholder dilution.
- Inability to Raise Capital: Difficulty securing future funding on acceptable terms could jeopardize operations.
- Lack of Profitability: No clear timeline to achieving profitability based on current revenue trajectory.
- Ongoing Losses and Cash Burn:Continued operating losses require ongoing funding, depleting cash reserves.
- Operational and Execution Risks:
- Manufacturing Scalability:Potential challenges in ensuring licensees can manufacture Akida-based SoCs reliably and cost-effectively at scale through foundry partners (TSMC, GlobalFoundries, IFS).
- Talent Acquisition and Retention:Difficulty attracting and retaining highly skilled engineers and sales personnel in competitive AI talent markets.
- US Redomiciling Execution:Risks associated with the complex process of redomiciling to the US and delisting from the ASX, including shareholder approval, regulatory hurdles, and market reception.
- Manufacturing Scalability:Potential challenges in ensuring licensees can manufacture Akida-based SoCs reliably and cost-effectively at scale through foundry partners (TSMC, GlobalFoundries, IFS).
- Intellectual Property and Legal Risks:
- IP Infringement:Risk of competitors infringing on BrainChip's patents or unauthorized use of its IP.
- IP Challenges:Potential for third parties to challenge the validity or ownership of BrainChip's IP.
- Trade Secret Protection:Risk of trade secrets being compromised.
- Litigation:Potential for disputes related to contracts, IP, or other matters.
- Disclosure Risks:Balancing market disclosure obligations (especially under ASX rules) with the need to protect commercially sensitive information under NDAs with partners.
- IP Infringement:Risk of competitors infringing on BrainChip's patents or unauthorized use of its IP.
- General Economic and Market Risks: Broader economic downturns, changes in investor sentiment towards technology stocks, inflation, interest rate movements, and geopolitical instability can impact the company's valuation and access to capital. The ongoing effects of global events like the COVID-19 pandemic could also pose risks.
BrainChip Holdings stands at a critical juncture. The company possesses innovative neuromorphic technology, Akida, with compelling theoretical advantages for the burgeoning edge AI market, particularly in ultra-low-power applications. Its event-based processing and on-chip learning capabilities differentiate it from conventional AI hardware. High-profile partnerships and engagements provide validation of the technology's potential in demanding sectors like automotive (concept), space, and defense.
However, the path to commercial success remains fraught with significant challenges and uncertainties. The transition from technology development and evaluation to generating substantial, recurring revenue through its IP licensing model has proven slower and more difficult than perhaps initially anticipated. The lack of material royalty revenue from key early licensees is a major concern, raising questions about the timelines and commercial viability of partners' Akida-based products. While recent contract wins in defense and space are positive developments, they are unlikely to provide the high volumes needed to achieve profitability in the near term.
The company's success over the next 12 months, and beyond, hinges critically on execution across several fronts:
- Akida 2.0 Rollout:Timely delivery of Akida 2.0 silicon and successful adoption by lead customers are paramount. This generation's features (8-bit, ViT/TENN support) appear necessary to compete effectively in the advanced edge AI market. Further significant delays could severely impact competitiveness.
- Commercial Traction: Converting the existing pipeline and partnerships into significant, high-volume IP license agreements, particularly in target commercial markets like consumer electronics, industrial IoT, or potentially automotive production programs, is essential. News of substantial new license deals or, crucially, the commencement of royalty streams from existing licensees (especially Renesas or MegaChips) would be major positive catalysts.
- Financial Management: Continued access to funding is necessary given the ongoing cash burn. Successful execution of the LDA Capital facility drawdowns and potentially other capital raises will be required, although this will likely involve further dilution. Progress towards reducing cash burn through revenue growth or cost control is vital.
- US Redomiciling:If pursued, the successful execution of the proposed move to a US stock exchange could potentially broaden the investor base and improve access to capital but also involves significant execution risk and cost.
BrainChip's market capitalization (currently around A$550M ) reflects a high degree of speculation regarding its future success rather than current financial performance. Over the next 12 months, its valuation is likely to be driven primarily by:
- News Flow: Announcements regarding new commercial partnerships, IP license agreements (especially with major semiconductor players or OEMs in high-volume markets), progress updates from existing licensees (e.g., Renesas product launch, royalty commencement), Akida 2.0 milestones (hardware availability, design wins), and significant contract awards (particularly beyond niche defense applications) will be key determinants. Positive news flow could drive significant upward momentum, while a lack of substantive progress could lead to further declines.
- Revenue Recognition: Any indication of meaningful revenue growth, particularly the start of royalty payments, would provide tangible validation of the business model and could significantly re-rate the stock. Conversely, continued minimal revenue will likely weigh heavily on valuation.
- Funding Activities: The terms and necessity of future capital raises, including drawdowns from the LDA facility, will influence market capitalization through dilution effects and signaling of financial health.
- Akida 2.0 Progress: Confirmation of AKD2000 hardware availability and initial customer adoption would be a critical milestone. Conversely, further delays would be a significant negative.
- Competitive Developments: Announcements or product launches from competitors (e.g., Intel's Loihi commercialization, advances in low-power conventional AI) could impact BrainChip's perceived competitive positioning.
- US Redomiciling Outcome: If the company proceeds, the success and market reception of the US listing process will be a major factor.
- Broader Market Sentiment: General sentiment towards AI, semiconductor stocks, and speculative growth companies will also influence BrainChip's valuation.
BrainChip Holdings presents a high-risk, potentially high-reward investment proposition. The Akida technology offers a genuinely innovative approach to AI processing at the edge, targeting critical needs for low power and on-device intelligence. The alignment with the large and rapidly growing Edge AI market is clear. However, the company faces formidable challenges in commercial execution, competition from established giants, and managing its financial resources.
Success is far from guaranteed and depends heavily on management's ability to convert technological promise and partnership potential into tangible, significant revenue streams in the relatively near future. The next 12 months are likely to be pivotal, with progress on Akida 2.0, securing major commercial design wins leading to royalties, and navigating the funding landscape being critical determinants of the company's trajectory and market valuation. Investors must weigh the substantial potential market opportunity against the significant execution risks and the company's current financial fragility. Predicting the market capitalization remains inherently uncertain and highly dependent on the successful achievement of key commercial and technological milestones.
Sources used in the report
PICO Via TENNs brings LLM's to the Edge.
BRN has barely scratched the surface with PICO. So much to look forward to.
It's only around 1sq mm.
Imagine what PICO can do now that Tony Lewis says we can migrate Traditional to State Space models (SSM). Pico runs on TENNs which is a type of SSM.
"The Future of AI on the Edge
The ability to run LLMs on the edge holds immense potential. Whether it’s in automotive systems, home appliances, or industrial equipment, BrainChip’s Akida Pico provides the intelligence needed to process data and respond in real-time without relying on cloud-based services. This has implications not just for improving device efficiency but also for lowering costs and enhancing data privacy.
As AI continues to evolve, BrainChip’s Akida Pico stands out as a pivotal development in the transition from cloud-reliant systems to powerful, independent edge AI solutions. This shift will undoubtedly reshape industries, making devices smarter, faster, and more efficient—without the need to pay for cloud services."
ipxchange.tech
BRN has barely scratched the surface with PICO. So much to look forward to.
It's only around 1sq mm.
Imagine what PICO can do now that Tony Lewis says we can migrate Traditional to State Space models (SSM). Pico runs on TENNs which is a type of SSM.
"The Future of AI on the Edge
The ability to run LLMs on the edge holds immense potential. Whether it’s in automotive systems, home appliances, or industrial equipment, BrainChip’s Akida Pico provides the intelligence needed to process data and respond in real-time without relying on cloud-based services. This has implications not just for improving device efficiency but also for lowering costs and enhancing data privacy.
As AI continues to evolve, BrainChip’s Akida Pico stands out as a pivotal development in the transition from cloud-reliant systems to powerful, independent edge AI solutions. This shift will undoubtedly reshape industries, making devices smarter, faster, and more efficient—without the need to pay for cloud services."
BrainChip’s Akida Pico Brings Large Language Models to the Edge | ipXchange
"Discover how BrainChip's Akida Pico is revolutionizing AI by bringing large language models (LLMs) to the edge. Learn how this neural processor eliminates cloud dependency, reduces costs, and enhances device efficiency in smart appliances and industrial IoT."
ipxchange.tech
Ok just watch Tony’s embedded world interview, Get ready for this people, IT WAS FUCKEN GREAT, did you watch it Hehir that’s how you interview, you show mystery, you show enthusiasm, you show I can’t believe it either, then you prove everything you just talked about,it was like a demtel add but wait there’s more. At last someone without a monotone boring,I’ll read from script interview. At fucken lastttttttt.
The Pope
Regular
He could take a free 30 day trial for premium LinkedIn that offers more transparency on who is looking at his LinkedIn profile that caters for budget conscious kiwis (your suggestion being a kiwi lol)Fair chance that he doesn’t. He’s a Kiwi after all
Hi manny,PICO Via TENNs brings LLM's to the Edge.
BRN has barely scratched the surface with PICO. So much to look forward to.
It's only around 1sq mm.
Imagine what PICO can do now that Tony Lewis says we can migrate Traditional to State Space models (SSM). Pico runs on TENNs which is a type of SSM.
"The Future of AI on the Edge
The ability to run LLMs on the edge holds immense potential. Whether it’s in automotive systems, home appliances, or industrial equipment, BrainChip’s Akida Pico provides the intelligence needed to process data and respond in real-time without relying on cloud-based services. This has implications not just for improving device efficiency but also for lowering costs and enhancing data privacy.
As AI continues to evolve, BrainChip’s Akida Pico stands out as a pivotal development in the transition from cloud-reliant systems to powerful, independent edge AI solutions. This shift will undoubtedly reshape industries, making devices smarter, faster, and more efficient—without the need to pay for cloud services."
BrainChip’s Akida Pico Brings Large Language Models to the Edge | ipXchange
"Discover how BrainChip's Akida Pico is revolutionizing AI by bringing large language models (LLMs) to the edge. Learn how this neural processor eliminates cloud dependency, reduces costs, and enhances device efficiency in smart appliances and industrial IoT."
The link is missing"-edge" at the end.
Thanks for posting. Most everything I skimmed and read I already generally knew. So I didn't find the response enlightening or revealing. No dis or slight meant toward you Five Bucks. I appreciate your effort.
However, I did come away with a question I would love for you to ask Gemini next. "Hello, please tell me how much electricity was consumed generating the massive answer and, or, how much coal was burned generating the response just given, and finally how much CO2 was released into the atmosphere as a result? "
Now THAT would be interesting,.....at least to me. We know the inquiries cost money and resources but do we know how much?
Regards, dippY
MDhere
Top 20
Almost took that tues morning flight but was worried about delays etc so took the night before and staying a few days. Your home flight not until late so plenty of time for an Irish coffee (or beer) in Sydney
Safe flight tuesday. You never know Dolci might be on your flight..perceptron
Regular
Thanks for posting. We need more videos like this from our company, actually explaining what they are currently doing. The interviewer here is correct, we need this technology to enhance our lives.
FiveBucks
Regular
Very little effort required on my part
The deep research model is quite amazing though. If someone is looking to research a new potential investment, it is a game changer. It produced a comprehensive report in 10 minutes.
FJ-215
Regular
Hi dippY,
Along with how much coal, I think you would also have to ask what type of coal.
From the USGS
" What are the types of coal?
There are four major types (or “ranks”) of coal. Rank refers to steps in a slow, natural process called “coalification,” during which buried plant matter changes into an ever denser, drier, more carbon-rich, and harder material. The four ranks are:
- Anthracite: The highest rank of coal. It is a hard, brittle, and black lustrous coal, often referred to as hard coal, containing a high percentage of fixed carbon and a low percentage of volatile matter.
- Bituminous: Bituminous coal is a middle rank coal between subbituminous and anthracite. Bituminous coal usually has a high heating (Btu) value and is used in electricity generation and steel making in the United States. Bituminous coal is blocky and appears shiny and smooth when you first see it, but look closer and you might see it has thin, alternating, shiny and dull layers.
- Subbituminous: Subbituminous coal is black in color and is mainly dull (not shiny). Subbituminous coal has low-to-moderate heating values and is mainly used in electricity generation.
- Lignite: Lignite coal, aka brown coal, is the lowest grade coal with the least concentration of carbon. Lignite has a low heating value and a high moisture content and is mainly used in electricity generation."
I see The Donald recently announcing that the US had discovered a 5th type of coal.
* Beautiful clean coal
Benefits include no harmful affects in the way of CO2, air pollution or respiratory diseases.
Can't find much research on it but said to be formed from the fossilized remains of unicorn poop.
Rskiff
Regular
Pom down under
Top 20
Hopefully everyone will be going somewhere for a beer lunchAlmost took that tues morning flight but was worried about delays etc so took the night before and staying a few days. Your home flight not until late so plenty of time for an Irish coffee (or beer) in Sydney
buena suerte :-)
BOB Bank of Brainchip
Fair to say we will get pummeled on open today.
buena suerte :-)
BOB Bank of Brainchip
Similar threads
