Intel ibm and brainchip…. Move on move on… nothing to see here…. Move onNice we still getting mentioned out there in recent articles / blogs as a leading company and Akida linked back to the BRN site.
Neuromorphic Computing: Future of Artificial Intelligence | CyberPro Magazine
The Science Behind Neuromorphic Computing: 1. Biological Inspiration 2. Key Principles and Concepts Neuromorphic Hardware 1. Neuromorphic Chips 2. Healthcare for more information readcybrpro.com
Neuromorphic Computing: Revolutionizing the Future of Artificial Intelligence
Imagine a world where computers can think, learn, and adapt just like the human brain. This is not a futuristic dream but a rapidly approaching reality, thanks to neuromorphic computing. As the boundaries of artificial intelligence (AI) and machine learning continue to expand, it emerges as a groundbreaking approach that promises to revolutionize how we process information. By mimicking the neural architecture of the human brain, this innovative technology aims to create more efficient, adaptive, and intelligent systems. In this article, we will explore the fascinating world of neuromorphic computing, uncovering its principles, applications, and the profound impact it is set to have on various industries.
What is Neuromorphic Computing?
Definition and Overview
It is an innovative approach to designing computer systems that mimic the human brain’s architecture and functioning. Unlike traditional computing systems that rely on binary logic and von Neumann architecture, it uses artificial neurons and synapses to process information more organically and efficiently.
History and Development
The concept of neuromorphic computing dates back to the 1980s when Carver Mead first introduced it. Over the years, significant advancements in neuroscience and materials science have propelled the development of neuromorphic systems, bringing us closer to creating machines that think and learn like humans.
The Science Behind Neuromorphic Computing
1. Biological Inspiration
It draws heavy inspiration from the structure and functioning of the human brain. The brain’s neural networks, consisting of neurons and synapses, process information in parallel, allowing for remarkable efficiency and adaptability.
2. Key Principles and Concepts
Key principles include the use of spiking neural networks (SNNs), which emulate the brain’s way of transmitting information through electrical spikes. This method not only enhances processing speed but also significantly reduces power consumption.
Neuromorphic Hardware
1. Neuromorphic Chips
-Source-techovedas.com_.jpg
At the heart of this computing are neuromorphic chips. These specialized processors are designed to replicate the brain’s neural networks, enabling efficient and real-time data processing. Leading examples include IBM’s TrueNorth and Intel’s Loihi chips.
2. Spiking Neural Networks (SNNs)
SNNs are a crucial component of this computing. Unlike traditional neural networks, SNNs use spikes or bursts of electrical activity to transmit information. This approach closely mirrors how biological neurons communicate, leading to more efficient and realistic processing.
Advantages
1 . Energy Efficiency
One of the most significant advantages is its energy efficiency. By mimicking the brain’s low-power consumption mechanisms, neuromorphic systems can operate with significantly less energy than conventional computers.
2. Real-time Processing
Neuromorphic systems excel in real-time data processing, making them ideal for applications that require immediate responses, such as robotics and autonomous vehicles.
Applications
1. Robotics
It has the potential to revolutionize robotics by enabling machines to process sensory information and make decisions more like humans. This can lead to more adaptive and intelligent robots capable of performing complex tasks.
2. Healthcare
In healthcare, neuromorphic systems can enhance medical imaging, diagnostics, and personalized treatment plans. Their ability to process vast amounts of data in real-time can lead to more accurate and timely medical interventions.
3. Autonomous Vehicles
For autonomous vehicles, it offer faster and more efficient data processing, improving decision-making and safety. These systems can handle complex sensory inputs, such as visual and auditory data, more effectively than traditional processors.
Internet of Things (IoT)
The integration of this computing inIoT devices can lead to smarter and more responsive environments. From smart homes to industrial automation, the possibilities are endless with neuromorphic-enhanced IoT systems.
Challenges
1. Technical Hurdles
Despite its potential, it faces several technical challenges. These include the complexity of designing neuromorphic chips and the need for new programming paradigms to leverage their capabilities fully.
2. Adoption Barriers
Adoption barriers also exist, such as the lack of standardization and the need for specialized knowledge to develop and implement neuromorphic systems. Overcoming these barriers will be crucial for widespread adoption.
Comparison with Traditional Computing
1. Speed and Efficiency
Neuromorphic systems offer superior speed and efficiency compared to traditional computers. Their parallel processing capabilities and low-power consumption make them ideal for tasks that require real-time responses.
2. Scalability
Scalability is another area where neuromorphic computing shines. Unlike traditional systems that struggle with scaling, neuromorphic architectures can easily expand to accommodate larger and more complex tasks.
Future Prospects of Neuromorphic Computing
1. Research and Development
Ongoing research and development in this computing are paving the way for even more advanced and efficient systems. Collaboration between neuroscientists, computer scientists, and engineers is driving innovation in this field.
2. Potential Impact on AI
The potential impact of this computing on AI is immense. By creating systems that learn and adapt like the human brain, we can develop more intelligent and capable AI applications that can solve complex problems in ways that traditional AI cannot.
Leading Companies
1. IBM
IBM is a pioneer in neuromorphic computing, with its TrueNorth chip leading the way. This chip features millions of artificial neurons and synapses, enabling advanced cognitive computing capabilities.
2. Intel
Intel’s Loihi chip is another significant player in the neuromorphic space. Loihi is designed to emulate the brain’s natural learning processes, making it ideal for applications that require adaptive and intelligent systems.
3. BrainChip Holdings
BrainChip Holdings is known for its Akida neuromorphic system, which offers real-time learning and inference capabilities. Akida is designed for edge AI applications, providing efficient and low-power solutions for various industries.
Case Studies and Real-world Examples
Case studies and real-world examples highlight the practical applications of this computing. From improving medical diagnostics to enhancing autonomous vehicle navigation, the impact of neuromorphic systems is being felt across various sectors.
Neuromorphic Computing and Ethics
1. Privacy Concerns
As with any advanced technology, it raises privacy concerns. The ability to process and analyze vast amounts of data in real-time necessitates robust privacy protections to ensure user data is safeguarded.
2. Ethical Implications
Ethical implications also arise from the use of neuromorphic systems. Questions about the potential for misuse and the need for ethical guidelines to govern their development and deployment are critical considerations.
Neuromorphic Computing in Education
1. Training and Development Programs
To foster growth, education, and training programs are essential. Universities and institutions are beginning to offer specialized courses and programs to equip the next generation of scientists and engineers with the skills needed to advance this field.
How to Get Started?
Resources and Learning Paths
For those interested in this computing, various resources and learning paths are available. Online courses, workshops, and research papers provide valuable insights and knowledge to help you get started in this exciting field.
FAQs
1. What is neuromorphic computing?
It is a field of computing that aims to mimic the neural architecture and functioning of the human brain to create more efficient and intelligent systems.
2. How does it differ from traditional computing?
Unlike traditional computing, which relies on binary logic and von Neumann architecture, it uses artificial neurons and synapses to process information in a way that closely resembles the human brain.
3. What are the advantages of neuromorphic computing?
It offers several advantages, including energy efficiency, real-time processing, and scalability, making it ideal for applications that require immediate and adaptive responses.
4. Which industries can benefit from neuromorphic computing?
Industries such as healthcare, robotics, autonomous vehicles, and the Internet of Things (IoT) can benefit significantly from the advancements in neuromorphic computing.
5. What are the challenges facing neuromorphic computing?
Challenges include technical hurdles in designing neuromorphic chips, adoption barriers, and the need for new programming paradigms to fully leverage the capabilities of
neuromorphic systems.
Conclusion
Neuromorphic computing represents a groundbreaking shift in how we approach computing and AI. By emulating the human brain’s architecture and functioning, neuromorphic systems offer unparalleled efficiency, real-time processing, and adaptability. As research and development continue to advance, the potential applications of this computing are vast, promising to revolutionize industries ranging from healthcare to autonomous vehicles. Embracing this technology and overcoming its challenges will pave the way for a smarter, more efficient future.
BRN Discussion Ongoing
- Thread starter TechGirl
- Start date
The bitter worm pokes his head up and pleads to be noticed. And to think you once had a great group of friends that trusted and liked you. That was until you revealed your true little worm self. Poor little worm, destined to travel the rest of his life along the dark worm hole between HC and TSex looking for self importance. What a worm!Good morning fellow chippers!
What a great news to wake up to.
New CMO. Looks like big changes are being made! Great move Sean and co!
OMG how exciting! This will surely work for us
Can't help to think that if I recall correctly, our previous CMO Nandan had a lot of experience. He was Ex-ARM and came from Amazon. He was touted as the one we need to drive our product marketing globally through his connections. This was 2 years ago and got a lot of share holders excited. Then he left quietly.
I'd give this new guy 2-3 years top.
GLTAH
Not advice
Lol. Who says worm?
Smoothsailing
Regular
Brilliant
wilzy123
Founding Member
Good morning fellow chippers!
What a great news to wake up to.
New CMO. Looks like big changes are being made! Great move Sean and co!
OMG how exciting! This will surely work for us
Can't help to think that if I recall correctly, our previous CMO Nandan had a lot of experience. He was Ex-ARM and came from Amazon. He was touted as the one we need to drive our product marketing globally through his connections. This was 2 years ago and got a lot of share holders excited. Then he left quietly.
I'd give this new guy 2-3 years top.
GLTAH
Not advice
Interns are great to bring on board. We keep the best and the others spread the BRN word when they move on.
Its the same with staff as they also spread the word when they move on.
Its only a matter of time before we get deals over the line IMO.
Tothemoon24
Top 20
My journey at BrainChip has reached an end and it’s now time for me to embark on a new adventure and explore exciting opportunities ahead. I have been incredibly fortunate to work alongside some of the brightest minds in the industry, contributing to groundbreaking innovations in AI and neuromorphic technology.
I want to extend my heartfelt thanks to my colleagues, mentors, and the entire BrainChip community for their support, guidance, and friendship. The experiences and skills I have gained here will undoubtedly shape my future career.
As I move forward, I am excited to explore new consultancy and leadership opportunities. I am driven to continue pushing the boundaries of technology and innovation, and I am eager to bring my expertise to new and challenging projects.
Thank you, BrainChip, for an incredible journey. Here's to new beginnings!
View attachment 67739
My journey at BrainChip has reached an end and it’s now time for me to embark on a new adventure and explore exciting opportunities ahead. I have been incredibly fortunate to work alongside some of the brightest minds in the industry, contributing to groundbreaking innovations in AI and neuromorphic technology.
I want to extend my heartfelt thanks to my colleagues, mentors, and the entire BrainChip community for their support, guidance, and friendship. The experiences and skills I have gained here will undoubtedly shape my future career.
As I move forward, I am excited to explore new consultancy and leadership opportunities. I am driven to continue pushing the boundaries of technology and innovation, and I am eager to bring my expertise to new and challenging projects.
Thank you, BrainChip, for an incredible journey. Here's to new beginnings!
Thanks for posting that...I'm pretty sure that Valentina was the Manager of the Research Institute here in Perth, the more I think
about the staff we had there, well, I wouldn't be at all surprised if some made it back to our company when (and if) we become
financially stable on our own two feet without the need to keep raising funds the discounted way.
Some great posts coming through, though the order of things should read:
BRAINCHIP HOLDINGS LTD (EDGE AI) Commercially Available Now.
INTEL CORP (AI) Lab Department.
IBM (AI) Server Farm.
Tech
Pom down under
Top 20
Thank you XRP
Pom down under
Top 20
Shouldn't this be an official ASX announcement?View attachment 67739
My journey at BrainChip has reached an end and it’s now time for me to embark on a new adventure and explore exciting opportunities ahead. I have been incredibly fortunate to work alongside some of the brightest minds in the industry, contributing to groundbreaking innovations in AI and neuromorphic technology.
I want to extend my heartfelt thanks to my colleagues, mentors, and the entire BrainChip community for their support, guidance, and friendship. The experiences and skills I have gained here will undoubtedly shape my future career.
As I move forward, I am excited to explore new consultancy and leadership opportunities. I am driven to continue pushing the boundaries of technology and innovation, and I am eager to bring my expertise to new and challenging projects.
Thank you, BrainChip, for an incredible journey. Here's to new beginnings!
I think this is huge news for us shareholders!
Pom down under
Top 20
The only thing huge is the gap between your earsShouldn't this be an official ASX announcement?
I think this is huge news for us shareholders!
Pom down under
Top 20
BrainChip– Internship Program 2024 - USC Viterbi | Career Services
BrainChip– Internship Opportunity! Apply Today! About BrainChip: BrainChip develops technology that brings commonsense to the processing of sensor data, allowing efficient use for AI inferencing enabling one to do more with less. Accurately. Elegantly. Meaningfully. They call this Essential AI...
viterbicareers.usc.edu
Pom down under
Top 20
Old news but still impressive to be mentioned with the elite
m.economictimes.com
While attrition fell by 50 bps to 12.4% in Q4 of FY24 from 12.9% in Q3, the company’s headcount increased by 178 to end the year at 13,399. It said that 25% of its entire talent base to be AI ready by Q3 FY25. It has already partnered with NVIDIA, AWS, Microsoft, Google, Intel, and Brainchip. Tata Elxsi’s AI Lab at Indian Institute of Science (IISc) is also equipped with NVIDIA GPU infrastructure for advanced research in AI/ML.
Tata Elxsi Q4 Results: Net down 4.5% QoQ to Rs 197 crore; company added 1,500 employees in FY24 - The Economic Times
The engineering service provider also saw its Q4 revenue at Rs. 905.9 crore, down by 0.9% QoQ. This the first time after Q1 of FY21, where the company saw a sequential revenue decline. For the full fiscal its revenue grew by 13% on-year at Rs 3,552 crore.
While attrition fell by 50 bps to 12.4% in Q4 of FY24 from 12.9% in Q3, the company’s headcount increased by 178 to end the year at 13,399. It said that 25% of its entire talent base to be AI ready by Q3 FY25. It has already partnered with NVIDIA, AWS, Microsoft, Google, Intel, and Brainchip. Tata Elxsi’s AI Lab at Indian Institute of Science (IISc) is also equipped with NVIDIA GPU infrastructure for advanced research in AI/ML.
Pom down under
Top 20
BrainChip included in the report
Last edited:
Great find. Tata Elxsi are a will use AKIDA. Tony said at the last AGM one big deal would see us profitable overnight.
We also know about the Mercedes tie up.
Mmmmm its hard not to feel a little positive.
Hope you do not mind but i pinched the Tata link you posted and dropped it over on the crapper just to 'put the wind up the downrampers'.
They will no doubt rapid fire to bury it. Usually after hours its one poster pulling out several alias accounts one after the other to do the burying.
Fullmoonfever
Top 20
Nice names to be next to....high risk high reward....red or black 
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The human brain consumes about 20 watts of power, which is roughly equivalent to a dim light bulb. Despite this low power consumption, it can perform an estimated 10^16 operations per second. In contrast, a high-end Nvidia GPU consumes about 400 watts of power to perform a “measly” 10^14 to 10^15 operations per second. This means the human brain is approximately 100,000 to 1,000,000 times more energy-efficient than modern GPUs.
But what if we could replicate that amazing efficiency on a physical computer chip? That’s the ambitious vision for neuromorphic computing. The neuromorphic computing market, while still in its infancy, is projected to grow by 21.2% annually (CAGR) over the next five years. So in this guide, we’ll explore the top neuromorphic computing stocks, ranked by pure-play focus.
The key growth drivers for the neuromorphic computing market stem from several high-impact sectors.. For example, the automotive industry is pushing for more advanced driver assistance systems and fully autonomous vehicles. These require real-time processing of vast amounts of sensory data with minimal latency and power consumption – an ideal use case for neuromorphic chips.
Mobile and edge AI is another huge demand driver. As AI capabilities become more important in smartphones and other mobile devices, neuromorphic chips offer a way to run sophisticated AI models without quickly draining batteries or requiring constant cloud connectivity.
That said, keep in the mind that neuromorphic computing is a bleeding edge technology. Creating chips that truly mimic the brain’s neuron and synapse structure at scale is an enormously complex task, and current neuromorphic chips are highly limited in their neuron count compared to the human brain. Also traditional software development approaches don’t translate directly to neuromorphic systems. This presents a major (and still unsolved) challenge in integration with existing computing ecosystems.
Note: We make every effort to keep our information accurate and up-to-date. However, technology markets do move fast and company situations can change rapidly. Please use this guide as an intro to the neuromorphic computing landscape; but ultimately, do your own due diligence before taking action.
Australia-based BrainChip is a pioneer in commercializing neuromorphic computing, focusing on edge AI solutions. The company has developed an Edge AI platform that combines innovative silicon IP, software, and machine learning. This platform includes the Akida neuromorphic processor. Akida is designed to process information in a way that mimics the human brain from a fundamental hardware level. This “imitation” goes beyond the deep neural networks used in today’s AI models.
Brainchip enjoys first-mover advantage in commercial neuromorphic computing. The company’s technology has several unique features, including microwatt power consumption and on-chip learning, while being able to support standard machine learning workflows. In fact, it offers a claimed 5-10x improvement in performance-per-watt over traditional AI accelerators. This would make the Akida chip ideal for battery-powered devices, edge computing, and in-sensor intelligence.
The company is pursuing a flexible business model centered on high-margin IP licensing. This strategy involves upfront license fees and ongoing royalties, which could provide steady revenue as adoption grows. BrainChip’s intellectual property portfolio includes 17 granted patents and 30 pending patents. The company’s team consists of 80% engineers, with 15% holding PhDs from leading AI research programs. BrainChip is also building partnerships with system integrators, including MegaChips, Prophesee, and SiFive.
Neuromorphic chips are ideal for machine vision, edge AI, and smart cities.
Intel’s Loihi neuromorphic chip represents a significant advancement in brain-inspired computing. The chip contains millions of artificial neurons and synapses, allowing it to process information in a manner similar to biological neural networks. This architecture enables Loihi to excel at tasks that involve learning and adapting to new information in real-time, a capability that traditional computing systems often struggle with.
One of Loihi’s key features is its energy efficiency. The chip can perform certain types of AI computations using up to 1,000 times less energy than conventional processors. This efficiency stems from its event-driven processing, where neurons only activate when they receive sufficient input, similar to how biological neurons function. This approach allows Loihi to handle complex cognitive tasks while consuming minimal power, making it particularly suitable for edge computing and IoT applications.
Loihi’s versatility has been evident in various research applications. Intel has used the chip to solve optimization problems, control robotic systems, and process sensory data. For example, Loihi has been applied to gesture recognition, real-time image classification, and even olfactory processing. The chip’s ability to learn and adapt quickly has also shown promise in creating AI systems that can operate effectively in dynamic, unpredictable environments. As Intel continues to refine and scale up its neuromorphic technology, Loihi could potentially bridge the gap between traditional computing and the complex, adaptive capabilities of biological brains.
IBM’s TrueNorth neuromorphic chip architecture represents a significant leap in brain-inspired computing. The chip consists of a network of neurosynaptic cores, each containing 256 neurons that can establish up to 256 connections with other neurons. This dense, interconnected structure allows TrueNorth to process information in a highly parallel manner, similar to biological neural networks. The architecture is particularly adept at handling sensory data and performing pattern recognition tasks.
TrueNorth’s energy efficiency is one of its standout features. The chip can perform complex cognitive tasks while consuming only a fraction of the power required by traditional processors. This efficiency is achieved through its event-driven design, where neurons only activate and consume energy when they receive sufficient input. As a result, TrueNorth can process billions of synaptic operations per second per watt, making it well-suited for deployment in power-constrained environments such as mobile devices and IoT sensors.
IBM has demonstrated TrueNorth’s capabilities in various real-world applications. The chip has been used for tasks such as real-time object recognition, audio processing, and complex data analysis. For instance, TrueNorth has been applied to analyze satellite imagery for disaster response, detect anomalies in financial transactions, and even assist in medical diagnosis. The chip’s ability to learn and adapt to new patterns makes it particularly useful in scenarios where data streams are constantly changing. As IBM continues to develop and scale its neuromorphic technology, TrueNorth could play a crucial role in advancing AI systems that can operate more efficiently and effectively in complex, real-world environments.
These phase change neurons store states in response to neuronal inputs. Credit: IBM Research
AMD, while primarily known for its CPUs and GPUs, could fit neuromorphic computing into its broader AI strategy. Keep in mind though, AMD’s current approach to neuromorphic computing is not publicized. The company is still focused more on general-purpose AI acceleration rather than specialized neuromorphic hardware. That said, there are natural synergies.
For example, AMD’s Instinct accelerators, designed for high-performance computing and AI, provide a platform for researchers to experiment with neuromorphic algorithms. While not strictly neuromorphic, these accelerators offer the power and flexibility needed to simulate large-scale spiking neural networks.
AMD has also developed software tools that could support neuromorphic computing research. For example, The company’s ROCm (Radeon Open Compute) platform includes libraries for deep learning and scientific computing that can be adapted for neuromorphic simulations.
Nvidia, known mainly for its GPUs, could also easily fit in neuromorphic computing as part of its broader AI strategy. Nvidia’s approach differs from the more specialized neuromorphic chips developed by Intel and IBM. Instead, Nvidia has been incorporating brain-inspired principles into its AI hardware and software stack.
For example, Nvidia has focused on developing hardware accelerators that can efficiently run spiking neural networks (SNNs). These networks are designed to more closely mimic the way biological neurons communicate, using discrete spikes of activity rather than continuous signals. This has allowed researchers to experiment with neuromorphic algorithms on widely available hardware.
In addition to hardware support, Nvidia has also developed software tools to facilitate neuromorphic computing research. The company’s CUDA Deep Neural Network library (cuDNN) has been extended to support sparse and event-driven computations. These types of computations are highly characteristic of neuromorphic systems. As AI continues to evolve, Nvidia will likely play a major role in bridging traditional deep learning approaches with more brain-like computing paradigms.
Qualcomm has been an early adopter of neuromorphic computing principles, with its efforts dating back to the Zeroth project. This initiative, launched in the early 2010s, aimed to develop a brain-inspired computing platform for mobile devices. The Zeroth chip was designed to process sensory data more efficiently by mimicking the human brain’s information processing methods.
Zeroth incorporated artificial neurons and synapses, creating a more brain-like processing architecture. This approach was intended to enable more natural and efficient handling of tasks like image recognition, speech processing, and other forms of sensory data analysis. One of the key goals was to create AI systems that could learn and adapt in real-time, similar to biological brains
The learnings from the Zeroth project have likely influenced Qualcomm’s ongoing AI and machine learning initiatives, particularly in the development of their Neural Processing Units (NPUs) for mobile devices. These NPUs, now integrated into Qualcomm’s mobile system-on-chip (SoC) designs, are designed to handle AI workloads more efficiently than traditional CPU or GPU architectures. While not fully neuromorphic, these NPU designs incorporate brain-inspired principles.
Top Neuromorphic Computing Stocks for 2024: Ranked by Pure-Play Focus
The human brain consumes about 20 watts of power, which is roughly equivalent to a dim light bulb. Despite this low power consumption, it can perform an estimated 10^16 operations per second. In contrast, a high-end Nvidia GPU consumes about 400 watts of power to perform a “measly” 10^14 to...
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Top Neuromorphic Computing Stocks for 2024: Ranked by Pure-Play Focus
- Last updated: August 6, 2024
The human brain consumes about 20 watts of power, which is roughly equivalent to a dim light bulb. Despite this low power consumption, it can perform an estimated 10^16 operations per second. In contrast, a high-end Nvidia GPU consumes about 400 watts of power to perform a “measly” 10^14 to 10^15 operations per second. This means the human brain is approximately 100,000 to 1,000,000 times more energy-efficient than modern GPUs.
But what if we could replicate that amazing efficiency on a physical computer chip? That’s the ambitious vision for neuromorphic computing. The neuromorphic computing market, while still in its infancy, is projected to grow by 21.2% annually (CAGR) over the next five years. So in this guide, we’ll explore the top neuromorphic computing stocks, ranked by pure-play focus.
The key growth drivers for the neuromorphic computing market stem from several high-impact sectors.. For example, the automotive industry is pushing for more advanced driver assistance systems and fully autonomous vehicles. These require real-time processing of vast amounts of sensory data with minimal latency and power consumption – an ideal use case for neuromorphic chips.
Mobile and edge AI is another huge demand driver. As AI capabilities become more important in smartphones and other mobile devices, neuromorphic chips offer a way to run sophisticated AI models without quickly draining batteries or requiring constant cloud connectivity.
That said, keep in the mind that neuromorphic computing is a bleeding edge technology. Creating chips that truly mimic the brain’s neuron and synapse structure at scale is an enormously complex task, and current neuromorphic chips are highly limited in their neuron count compared to the human brain. Also traditional software development approaches don’t translate directly to neuromorphic systems. This presents a major (and still unsolved) challenge in integration with existing computing ecosystems.
Note: We make every effort to keep our information accurate and up-to-date. However, technology markets do move fast and company situations can change rapidly. Please use this guide as an intro to the neuromorphic computing landscape; but ultimately, do your own due diligence before taking action.
Tier 1: Pure-Play Neuromorphic Computing Stocks
The pure-play neuromorphic computing stocks represent the cutting edge of this nascent industry. These companies stake their entire business model on the potential of these brain-inspired chips. While this focus creates higher risk, it also offers the most direct exposure for investors bullish on neuromorphic computing. With only one public company currently in this tier, it underscores just how early we are in the neuromorphic computing market.BrainChip Holdings (ASX: BRN)
BrainChip Holdings (ASX: BRN) is a first-mover in commercial neuromorphic computing, with a focus on energy-efficient edge AI.Australia-based BrainChip is a pioneer in commercializing neuromorphic computing, focusing on edge AI solutions. The company has developed an Edge AI platform that combines innovative silicon IP, software, and machine learning. This platform includes the Akida neuromorphic processor. Akida is designed to process information in a way that mimics the human brain from a fundamental hardware level. This “imitation” goes beyond the deep neural networks used in today’s AI models.
Brainchip enjoys first-mover advantage in commercial neuromorphic computing. The company’s technology has several unique features, including microwatt power consumption and on-chip learning, while being able to support standard machine learning workflows. In fact, it offers a claimed 5-10x improvement in performance-per-watt over traditional AI accelerators. This would make the Akida chip ideal for battery-powered devices, edge computing, and in-sensor intelligence.
The company is pursuing a flexible business model centered on high-margin IP licensing. This strategy involves upfront license fees and ongoing royalties, which could provide steady revenue as adoption grows. BrainChip’s intellectual property portfolio includes 17 granted patents and 30 pending patents. The company’s team consists of 80% engineers, with 15% holding PhDs from leading AI research programs. BrainChip is also building partnerships with system integrators, including MegaChips, Prophesee, and SiFive.
Tier 2: Tech Giants with Neuromorphic Initiatives
This tier features established tech giants making strategy investments in neuromorphic computing. These companies offer a blend of stability and innovation. The neuromorphic projects from these companies benefit from substantial R&D budgets and existing technological ecosystems. However, neuromorphic computing remains a small part of their overall business. This tier reveals how seriously major players view the potential of brain-inspired chips, even if commercialization is still years out.Intel Corporation (INTC)
Intel (INTC) developed Loihi, a neuromorphic chip with millions of artificial neurons that excels at real-time learning and adaptation.Intel’s Loihi neuromorphic chip represents a significant advancement in brain-inspired computing. The chip contains millions of artificial neurons and synapses, allowing it to process information in a manner similar to biological neural networks. This architecture enables Loihi to excel at tasks that involve learning and adapting to new information in real-time, a capability that traditional computing systems often struggle with.
One of Loihi’s key features is its energy efficiency. The chip can perform certain types of AI computations using up to 1,000 times less energy than conventional processors. This efficiency stems from its event-driven processing, where neurons only activate when they receive sufficient input, similar to how biological neurons function. This approach allows Loihi to handle complex cognitive tasks while consuming minimal power, making it particularly suitable for edge computing and IoT applications.
Loihi’s versatility has been evident in various research applications. Intel has used the chip to solve optimization problems, control robotic systems, and process sensory data. For example, Loihi has been applied to gesture recognition, real-time image classification, and even olfactory processing. The chip’s ability to learn and adapt quickly has also shown promise in creating AI systems that can operate effectively in dynamic, unpredictable environments. As Intel continues to refine and scale up its neuromorphic technology, Loihi could potentially bridge the gap between traditional computing and the complex, adaptive capabilities of biological brains.
IBM (IBM)
IBM (IBM) created TrueNorth, an energy-efficient neuromorphic chip capable of processing billions of synaptic operations per second per watt.IBM’s TrueNorth neuromorphic chip architecture represents a significant leap in brain-inspired computing. The chip consists of a network of neurosynaptic cores, each containing 256 neurons that can establish up to 256 connections with other neurons. This dense, interconnected structure allows TrueNorth to process information in a highly parallel manner, similar to biological neural networks. The architecture is particularly adept at handling sensory data and performing pattern recognition tasks.
TrueNorth’s energy efficiency is one of its standout features. The chip can perform complex cognitive tasks while consuming only a fraction of the power required by traditional processors. This efficiency is achieved through its event-driven design, where neurons only activate and consume energy when they receive sufficient input. As a result, TrueNorth can process billions of synaptic operations per second per watt, making it well-suited for deployment in power-constrained environments such as mobile devices and IoT sensors.
IBM has demonstrated TrueNorth’s capabilities in various real-world applications. The chip has been used for tasks such as real-time object recognition, audio processing, and complex data analysis. For instance, TrueNorth has been applied to analyze satellite imagery for disaster response, detect anomalies in financial transactions, and even assist in medical diagnosis. The chip’s ability to learn and adapt to new patterns makes it particularly useful in scenarios where data streams are constantly changing. As IBM continues to develop and scale its neuromorphic technology, TrueNorth could play a crucial role in advancing AI systems that can operate more efficiently and effectively in complex, real-world environments.
These phase change neurons store states in response to neuronal inputs. Credit: IBM Research
Tier 3: AI Chipmakers with Neuromorphic Research
The final tier consists of AI chip companies exploring neuromorphic principles. These companies provide more indirect exposure to the field. While not fully committed to neuromorphic designs, these companies are incorporating brain-inspired elements into their existing AI hardware. This tier illustrates how the lines between traditional AI acceleration and neuromorphic computing are blurring, potentially leading to hybrid approaches that could accelerate adoption.Advanced Micro Devices (AMD)
Advanced Micro Devices (AMD) leverages its high-performance computing platforms to support neuromorphic computing research.AMD, while primarily known for its CPUs and GPUs, could fit neuromorphic computing into its broader AI strategy. Keep in mind though, AMD’s current approach to neuromorphic computing is not publicized. The company is still focused more on general-purpose AI acceleration rather than specialized neuromorphic hardware. That said, there are natural synergies.
For example, AMD’s Instinct accelerators, designed for high-performance computing and AI, provide a platform for researchers to experiment with neuromorphic algorithms. While not strictly neuromorphic, these accelerators offer the power and flexibility needed to simulate large-scale spiking neural networks.
AMD has also developed software tools that could support neuromorphic computing research. For example, The company’s ROCm (Radeon Open Compute) platform includes libraries for deep learning and scientific computing that can be adapted for neuromorphic simulations.
Nvidia (NVDA)
Nvidia (NVDA) incorporates brain-inspired principles into its AI hardware and software stack, supporting efficient spiking neural networks.Nvidia, known mainly for its GPUs, could also easily fit in neuromorphic computing as part of its broader AI strategy. Nvidia’s approach differs from the more specialized neuromorphic chips developed by Intel and IBM. Instead, Nvidia has been incorporating brain-inspired principles into its AI hardware and software stack.
For example, Nvidia has focused on developing hardware accelerators that can efficiently run spiking neural networks (SNNs). These networks are designed to more closely mimic the way biological neurons communicate, using discrete spikes of activity rather than continuous signals. This has allowed researchers to experiment with neuromorphic algorithms on widely available hardware.
In addition to hardware support, Nvidia has also developed software tools to facilitate neuromorphic computing research. The company’s CUDA Deep Neural Network library (cuDNN) has been extended to support sparse and event-driven computations. These types of computations are highly characteristic of neuromorphic systems. As AI continues to evolve, Nvidia will likely play a major role in bridging traditional deep learning approaches with more brain-like computing paradigms.
Qualcomm (QCOM)
Qualcomm (QCOM) is an early adopter of neuromorphic computing principles, influencing the development of its NPUs for mobile devices.Qualcomm has been an early adopter of neuromorphic computing principles, with its efforts dating back to the Zeroth project. This initiative, launched in the early 2010s, aimed to develop a brain-inspired computing platform for mobile devices. The Zeroth chip was designed to process sensory data more efficiently by mimicking the human brain’s information processing methods.
Zeroth incorporated artificial neurons and synapses, creating a more brain-like processing architecture. This approach was intended to enable more natural and efficient handling of tasks like image recognition, speech processing, and other forms of sensory data analysis. One of the key goals was to create AI systems that could learn and adapt in real-time, similar to biological brains
The learnings from the Zeroth project have likely influenced Qualcomm’s ongoing AI and machine learning initiatives, particularly in the development of their Neural Processing Units (NPUs) for mobile devices. These NPUs, now integrated into Qualcomm’s mobile system-on-chip (SoC) designs, are designed to handle AI workloads more efficiently than traditional CPU or GPU architectures. While not fully neuromorphic, these NPU designs incorporate brain-inspired principles.
Private Neuromorphic Companies to Watch
Neuromorphic computing is an incredibly young field, and many of the most promising startups are still private. In addition to BrainChip (which is already public), we cover five other innovative startups—SynSense, GrAI Matter Lab, Prophesee, Innatera, and MemComputing—on this page.
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Was just over on the other site for a squiz.
Interesting comment by @Csharmo who I don't think is over here (?) who feels we are working with Wabtec.
Never looked at them myself but did a very quick google and this Dir popped up. Not looked too deeply yet.
Now....is he just a SH or maybe finds some interest in a recent BRN post.fron 3 weeks ago re the Neurobus, Frontgrade, Airbus consortium
www.wabteccorp.com
Interesting comment by @Csharmo who I don't think is over here (?) who feels we are working with Wabtec.
Never looked at them myself but did a very quick google and this Dir popped up. Not looked too deeply yet.
Now....is he just a SH or maybe finds some interest in a recent BRN post.fron 3 weeks ago re the Neurobus, Frontgrade, Airbus consortium
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So....all these changes.
New CMO, Peter out of retirement, reconstituting the SAB, shuttering Perth.
Raising >$20m
A direct offering that may have fallen flat.
What does this all mean for the trajectory of BrainChip?? What's next?
For a thread with a multitude of opinions, to my mind, has been very quiet.
New CMO, Peter out of retirement, reconstituting the SAB, shuttering Perth.
Raising >$20m
A direct offering that may have fallen flat.
What does this all mean for the trajectory of BrainChip?? What's next?
For a thread with a multitude of opinions, to my mind, has been very quiet.
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