BRN Discussion Ongoing

[Deleted member 3781]

With so many positive things happening in the business at the moment it is hard to fathom why the share price continues to sink.

It’s a complete joke

 

[manny100]

This is the reason why the price is dropping shorts over 100 million plus, where they are getting the shares from???NAKED bet yah and the fact there will be another capital raise in the future to support managements high burn rate of cash. View attachment 92768

Every knows there will be cap raises until revenue is sufficient to self sustain. So there is no surprise with that at all.
I am surprised that you are surprised.
The price is still within the range above the base. We have been to the bottom of the range only a relatively few times over the last 2 years. If we get there again it will only rise off that.
So what is the problem.

 

[HopalongPetrovski]

Hi all.

I asked Grok to "contrast and compare Brainchip's Akida technology with Nanoveu's ECS-DoT AI SoC."

I have a finger in both pies and thought perhaps others may be interested as well.
For what it is worth, here below is it's answer.




Overview

BrainChip's Akida and Nanoveu's ECS-DoT (developed by its subsidiary EMASS) are both ultra-low-power System-on-Chip (SoC) solutions designed for edge AI applications, targeting always-on processing in power-constrained environments like wearables, IoT devices, drones, and industrial sensors. Akida emphasizes neuromorphic computing, mimicking the brain's sparse, event-based processing for efficiency in neural network tasks. In contrast, ECS-DoT focuses on traditional RISC-V-based AI acceleration with non-volatile memory optimizations, prioritizing multimodal sensor fusion and benchmark-proven energy savings. While both enable cloud-free inference, Akida excels in bio-inspired learning, whereas ECS-DoT offers superior raw efficiency in standardized tests.

Key Comparison Table

Aspect	BrainChip Akida	Nanoveu ECS-DoT (EMASS)
Architecture	Neuromorphic (Spiking Neural Networks - SNNs); event-based, digital neuron fabric with sparsity exploitation (neurons fire only on thresholds). Supports CNNs, DNNs, RNNs, ViTs.	RISC-V core with AI accelerators and non-volatile memory (e.g., ReRAM integration); optimized for multimodal fusion (vision, audio, sensors). Traditional ANN focus.
Power Consumption	Ultra-low: ~1 mW (Akida Pico variant); milliwatt-scale for inference. Leverages sparsity for energy savings.	Milliwatt-scale (0.1–10 mW); benchmarks show 90% less energy vs. competitors (e.g., 0.8 µJ/inference in anomaly detection, 20% lower overall vs. peers).
Performance	Up to 1.2M neurons, 10B synapses per chip; scalable to 1,024 chips (1.2B neurons total). 8-bit weights/activations; low latency via multi-pass processing.	Up to 30 GOPS/W; 93% faster execution vs. competitors (e.g., 1.22 ms in anomaly detection, 3.9 ms in keyword spotting). 4 MB on-board SRAM for efficient compute.
Memory	Configurable local scratchpads; supports LPDDR4 SDRAM (e.g., 256M x 16 bytes in dev kits).	4 MB on-board SRAM; non-volatile tech reduces leakage and enables always-on modes.
Learning/Training	On-chip edge learning via reinforcement/inhibition; incremental learning supported.	Primarily inference-focused; on-device training not emphasized (relies on cloud/offline optimization).
Interfaces/Connectivity	PCIe 2.0, ARM Cortex-M4 (300 MHz), GPIO; multi-chip fabric for scaling.	Sensor-integrated (vision/audio); SDKs for IoT integration; partnerships for reference designs.
Applications	Edge vision (e.g., industrial inspection), voice, vibration; automotive, consumer electronics, IoT. Strong in pattern recognition.	Drones (extended flight time), wearables, healthcare (biometrics), smart cities; excels in real-time 2D-to-3D conversion, anomaly detection.
Process Node	28 nm (AKD1000); considering 14 nm.	Not specified; modular for future scaling to 6 nm/4 nm.
Software Ecosystem	MetaTF framework (TensorFlow/Keras integration); Edge Impulse support; cloud dev tools.	Enhanced SDKs/reference designs via Arrow Electronics; RISC-V tools for custom AI.
Maturity/Availability	Commercial since 2022 (AKD1000); dev kits (PCIe/Raspberry Pi) available; partnerships (e.g., Edge Impulse).	Emerged from stealth in 2025; benchmarks completed, OEM integrations underway (e.g., drones); sales reps appointed.
Strengths	Bio-mimetic efficiency for sparse data; on-device adaptability; scalable for larger networks.	Benchmark dominance in speed/energy; multimodal versatility; thermal efficiency (no cooling needed).
Challenges	Higher power in dense workloads; neuromorphic requires model optimization.	Less emphasis on learning; newer market entry may limit ecosystem breadth.

Similarities

• Ultra-Low-Power Edge Focus: Both target milliwatt-level operation for battery-powered, always-on AI, reducing cloud dependency, latency, and privacy risks. They enable real-time sensor processing (e.g., vision/audio) in constrained devices.
• Efficiency-Driven Design: Prioritize sparsity/exploitation of data patterns (Akida via neurons; ECS-DoT via memory optimizations) for 10–100x energy savings over general-purpose chips.
• Scalability and Integration: Modular for multi-chip or application-specific variants; support standard ML workflows (e.g., TensorFlow) and dev tools for rapid deployment.
• Target Markets: Overlap in IoT, wearables, drones, and industrial uses, addressing the growing edge AI market (projected >$20B by 2028).

Differences

• Core Philosophy: Akida's neuromorphic approach provides brain-like adaptability and on-chip learning, ideal for dynamic, sparse environments (e.g., event-driven sensors). ECS-DoT's RISC-V foundation delivers predictable, high-throughput performance in multimodal tasks, with superior benchmark results against incumbents like STMicroelectronics or Syntiant.
• Efficiency Metrics: ECS-DoT claims quantifiable edges (e.g., 287x faster in anomaly detection), positioning it for ultra-constrained apps like extended drone flights. Akida shines in qualitative sparsity gains but lacks direct head-to-head benchmarks here.
• Maturity and Ecosystem: Akida is more established with commercial products and broader partnerships, suiting immediate prototyping. ECS-DoT, as a 2025 newcomer, leverages Nanoveu's display tech synergies (e.g., 3D imaging) but focuses on rapid OEM adoption via distributors like Arrow.
• Innovation Trade-offs: Akida enables unique features like incremental learning for evolving models, while ECS-DoT prioritizes raw speed and thermal simplicity for "set-and-forget" deployments.

In summary, choose Akida for adaptive, brain-inspired AI in vision-heavy or learning-centric apps; opt for ECS-DoT for energy-critical, multimodal inference where benchmarks matter most. Both advance edge AI, but ECS-DoT's recent benchmarks suggest it could disrupt in power-sensitive niches.

 

[manny100]

For those unaware .

The ASX recently changed the reporting of short positions to save the reporting participants time and money on what they claimed as information that no one used . This eliminated access to the net short positions that was considered more accurate than the gross numbers as used by Shortman for example .


View attachment 92770


The below is an example of how retail are now exposed to false market information regarding short positions . Over this small example on Brainchip you can clearly see the current issue with accuracy . Due to this new level of abusive corruption in my opinion , I am currently seeking with the ASX to return to the previous method of reporting and to reinstate access to the more accurate net positions .

Note : Anyone thinking Brainchip currently has 100+ million being shorted and that professional shorts are going to be BurNt you may need to do some extra research , especially if you are an amateur retail invest with a current short position relying on the pro’s to protect you .

View attachment 92771

All in my opinion …..

Agree, gross shorts alone conceal the true directional position (market bet) of a fund. Retail investors do not know what the funds are ultimately betting on. Funds have different strategies eg may have % of shorts to hedge against the long bet but we only see the gross shorts not the long or the net.
If the net position is no where as 'bad' as the gross shorts suggest it actually encourages selling. Retailers can be 'spooked' - hence downrampers on forums.
There are many, many reasons why funds short to profit. For example even those who hold from lower prices and believe in the longer term prospects short price spikes to make some cash on the retrace. We only see the gross shorts figure though.
We saw a recent quick paced rise to 27 cents which was way overbought in relation to the time it took to get there. That was a certainty to be sold/shorted big.
A good news catalyst would be good right now.

 

[Diogenese]

For those unaware .

The ASX recently changed the reporting of short positions to save the reporting participants time and money on what they claimed as information that no one used . This eliminated access to the net short positions that was considered more accurate than the gross numbers as used by Shortman for example .


View attachment 92770


The below is an example of how retail are now exposed to false market information regarding short positions . Over this small example on Brainchip you can clearly see the current issue with accuracy . Due to this new level of abusive corruption in my opinion , I am currently seeking with the ASX to return to the previous method of reporting and to reinstate access to the more accurate net positions .

Note : Anyone thinking Brainchip currently has 100+ million being shorted and that professional shorts are going to be BurNt you may need to do some extra research , especially if you are an amateur retail investor with a current short position relying on the pro’s to protect you .

View attachment 92771

All in my opinion …..

So the gamekeeper is giving the poacher a leg up over the fence?

 

[Pom down under]

With so many positive things happening in the business at the moment it is hard to fathom why the share price continues to sink.

One major announcement and expecting us to hit 0.10 in no time

 

[manny100]

Hi all.

I asked Grok to "contrast and compare Brainchip's Akida technology with Nanoveu's ECS-DoT AI SoC."

I have a finger in both pies and thought perhaps others may be interested as well.
For what it is worth, here below is it's answer.




Overview

BrainChip's Akida and Nanoveu's ECS-DoT (developed by its subsidiary EMASS) are both ultra-low-power System-on-Chip (SoC) solutions designed for edge AI applications, targeting always-on processing in power-constrained environments like wearables, IoT devices, drones, and industrial sensors. Akida emphasizes neuromorphic computing, mimicking the brain's sparse, event-based processing for efficiency in neural network tasks. In contrast, ECS-DoT focuses on traditional RISC-V-based AI acceleration with non-volatile memory optimizations, prioritizing multimodal sensor fusion and benchmark-proven energy savings. While both enable cloud-free inference, Akida excels in bio-inspired learning, whereas ECS-DoT offers superior raw efficiency in standardized tests.

Key Comparison Table

Aspect	BrainChip Akida	Nanoveu ECS-DoT (EMASS)
Architecture	Neuromorphic (Spiking Neural Networks - SNNs); event-based, digital neuron fabric with sparsity exploitation (neurons fire only on thresholds). Supports CNNs, DNNs, RNNs, ViTs.	RISC-V core with AI accelerators and non-volatile memory (e.g., ReRAM integration); optimized for multimodal fusion (vision, audio, sensors). Traditional ANN focus.
Power Consumption	Ultra-low: ~1 mW (Akida Pico variant); milliwatt-scale for inference. Leverages sparsity for energy savings.	Milliwatt-scale (0.1–10 mW); benchmarks show 90% less energy vs. competitors (e.g., 0.8 µJ/inference in anomaly detection, 20% lower overall vs. peers).
Performance	Up to 1.2M neurons, 10B synapses per chip; scalable to 1,024 chips (1.2B neurons total). 8-bit weights/activations; low latency via multi-pass processing.	Up to 30 GOPS/W; 93% faster execution vs. competitors (e.g., 1.22 ms in anomaly detection, 3.9 ms in keyword spotting). 4 MB on-board SRAM for efficient compute.
Memory	Configurable local scratchpads; supports LPDDR4 SDRAM (e.g., 256M x 16 bytes in dev kits).	4 MB on-board SRAM; non-volatile tech reduces leakage and enables always-on modes.
Learning/Training	On-chip edge learning via reinforcement/inhibition; incremental learning supported.	Primarily inference-focused; on-device training not emphasized (relies on cloud/offline optimization).
Interfaces/Connectivity	PCIe 2.0, ARM Cortex-M4 (300 MHz), GPIO; multi-chip fabric for scaling.	Sensor-integrated (vision/audio); SDKs for IoT integration; partnerships for reference designs.
Applications	Edge vision (e.g., industrial inspection), voice, vibration; automotive, consumer electronics, IoT. Strong in pattern recognition.	Drones (extended flight time), wearables, healthcare (biometrics), smart cities; excels in real-time 2D-to-3D conversion, anomaly detection.
Process Node	28 nm (AKD1000); considering 14 nm.	Not specified; modular for future scaling to 6 nm/4 nm.
Software Ecosystem	MetaTF framework (TensorFlow/Keras integration); Edge Impulse support; cloud dev tools.	Enhanced SDKs/reference designs via Arrow Electronics; RISC-V tools for custom AI.
Maturity/Availability	Commercial since 2022 (AKD1000); dev kits (PCIe/Raspberry Pi) available; partnerships (e.g., Edge Impulse).	Emerged from stealth in 2025; benchmarks completed, OEM integrations underway (e.g., drones); sales reps appointed.
Strengths	Bio-mimetic efficiency for sparse data; on-device adaptability; scalable for larger networks.	Benchmark dominance in speed/energy; multimodal versatility; thermal efficiency (no cooling needed).
Challenges	Higher power in dense workloads; neuromorphic requires model optimization.	Less emphasis on learning; newer market entry may limit ecosystem breadth.

Similarities

• Ultra-Low-Power Edge Focus: Both target milliwatt-level operation for battery-powered, always-on AI, reducing cloud dependency, latency, and privacy risks. They enable real-time sensor processing (e.g., vision/audio) in constrained devices.
• Efficiency-Driven Design: Prioritize sparsity/exploitation of data patterns (Akida via neurons; ECS-DoT via memory optimizations) for 10–100x energy savings over general-purpose chips.
• Scalability and Integration: Modular for multi-chip or application-specific variants; support standard ML workflows (e.g., TensorFlow) and dev tools for rapid deployment.
• Target Markets: Overlap in IoT, wearables, drones, and industrial uses, addressing the growing edge AI market (projected >$20B by 2028).

Differences

• Core Philosophy: Akida's neuromorphic approach provides brain-like adaptability and on-chip learning, ideal for dynamic, sparse environments (e.g., event-driven sensors). ECS-DoT's RISC-V foundation delivers predictable, high-throughput performance in multimodal tasks, with superior benchmark results against incumbents like STMicroelectronics or Syntiant.
• Efficiency Metrics: ECS-DoT claims quantifiable edges (e.g., 287x faster in anomaly detection), positioning it for ultra-constrained apps like extended drone flights. Akida shines in qualitative sparsity gains but lacks direct head-to-head benchmarks here.
• Maturity and Ecosystem: Akida is more established with commercial products and broader partnerships, suiting immediate prototyping. ECS-DoT, as a 2025 newcomer, leverages Nanoveu's display tech synergies (e.g., 3D imaging) but focuses on rapid OEM adoption via distributors like Arrow.
• Innovation Trade-offs: Akida enables unique features like incremental learning for evolving models, while ECS-DoT prioritizes raw speed and thermal simplicity for "set-and-forget" deployments.

In summary, choose Akida for adaptive, brain-inspired AI in vision-heavy or learning-centric apps; opt for ECS-DoT for energy-critical, multimodal inference where benchmarks matter most. Both advance edge AI, but ECS-DoT's recent benchmarks suggest it could disrupt in power-sensitive niches.

Thanks, AKIDA will be favoured where on chip learning is needed eg, some Defense, Health space and robotics etc applications. I think we may in time find hybrid chips which include AKIDA and another chip for its qualities.

 

[Pom down under]

This is the reason why the price is dropping shorts over 100 million plus, where they are getting the shares from???NAKED bet yah and the fact there will be another capital raise in the future to support managements high burn rate of cash. View attachment 92768

Another cap raise must be close and I’m guessing the shorters are taking such high position knowing that they will be able to cover these back from LDA probably. Let’s just hope we get a decent $$$ announcement fall before the share price does.

 

[Deleted member 3781]

It will an explosive joy to see the shorts burn.. come on Sean… what’s the deal here?

 

[Pom down under]

It will an explosive joy to see the shorts burn.. come on Sean… what’s the deal here?

He must supports the shorters and is secretly accumulating, as there can’t be any other reason why our SP is where is is currently

 

[HopalongPetrovski]

Hi all.

I asked Grok to "contrast and compare Brainchip's Akida technology with Nanoveu's ECS-DoT AI SoC."

I have a finger in both pies and thought perhaps others may be interested as well.
For what it is worth, here below is it's answer.




Overview

BrainChip's Akida and Nanoveu's ECS-DoT (developed by its subsidiary EMASS) are both ultra-low-power System-on-Chip (SoC) solutions designed for edge AI applications, targeting always-on processing in power-constrained environments like wearables, IoT devices, drones, and industrial sensors. Akida emphasizes neuromorphic computing, mimicking the brain's sparse, event-based processing for efficiency in neural network tasks. In contrast, ECS-DoT focuses on traditional RISC-V-based AI acceleration with non-volatile memory optimizations, prioritizing multimodal sensor fusion and benchmark-proven energy savings. While both enable cloud-free inference, Akida excels in bio-inspired learning, whereas ECS-DoT offers superior raw efficiency in standardized tests.

Key Comparison Table

Aspect	BrainChip Akida	Nanoveu ECS-DoT (EMASS)
Architecture	Neuromorphic (Spiking Neural Networks - SNNs); event-based, digital neuron fabric with sparsity exploitation (neurons fire only on thresholds). Supports CNNs, DNNs, RNNs, ViTs.	RISC-V core with AI accelerators and non-volatile memory (e.g., ReRAM integration); optimized for multimodal fusion (vision, audio, sensors). Traditional ANN focus.
Power Consumption	Ultra-low: ~1 mW (Akida Pico variant); milliwatt-scale for inference. Leverages sparsity for energy savings.	Milliwatt-scale (0.1–10 mW); benchmarks show 90% less energy vs. competitors (e.g., 0.8 µJ/inference in anomaly detection, 20% lower overall vs. peers).
Performance	Up to 1.2M neurons, 10B synapses per chip; scalable to 1,024 chips (1.2B neurons total). 8-bit weights/activations; low latency via multi-pass processing.	Up to 30 GOPS/W; 93% faster execution vs. competitors (e.g., 1.22 ms in anomaly detection, 3.9 ms in keyword spotting). 4 MB on-board SRAM for efficient compute.
Memory	Configurable local scratchpads; supports LPDDR4 SDRAM (e.g., 256M x 16 bytes in dev kits).	4 MB on-board SRAM; non-volatile tech reduces leakage and enables always-on modes.
Learning/Training	On-chip edge learning via reinforcement/inhibition; incremental learning supported.	Primarily inference-focused; on-device training not emphasized (relies on cloud/offline optimization).
Interfaces/Connectivity	PCIe 2.0, ARM Cortex-M4 (300 MHz), GPIO; multi-chip fabric for scaling.	Sensor-integrated (vision/audio); SDKs for IoT integration; partnerships for reference designs.
Applications	Edge vision (e.g., industrial inspection), voice, vibration; automotive, consumer electronics, IoT. Strong in pattern recognition.	Drones (extended flight time), wearables, healthcare (biometrics), smart cities; excels in real-time 2D-to-3D conversion, anomaly detection.
Process Node	28 nm (AKD1000); considering 14 nm.	Not specified; modular for future scaling to 6 nm/4 nm.
Software Ecosystem	MetaTF framework (TensorFlow/Keras integration); Edge Impulse support; cloud dev tools.	Enhanced SDKs/reference designs via Arrow Electronics; RISC-V tools for custom AI.
Maturity/Availability	Commercial since 2022 (AKD1000); dev kits (PCIe/Raspberry Pi) available; partnerships (e.g., Edge Impulse).	Emerged from stealth in 2025; benchmarks completed, OEM integrations underway (e.g., drones); sales reps appointed.
Strengths	Bio-mimetic efficiency for sparse data; on-device adaptability; scalable for larger networks.	Benchmark dominance in speed/energy; multimodal versatility; thermal efficiency (no cooling needed).
Challenges	Higher power in dense workloads; neuromorphic requires model optimization.	Less emphasis on learning; newer market entry may limit ecosystem breadth.

Similarities

• Ultra-Low-Power Edge Focus: Both target milliwatt-level operation for battery-powered, always-on AI, reducing cloud dependency, latency, and privacy risks. They enable real-time sensor processing (e.g., vision/audio) in constrained devices.
• Efficiency-Driven Design: Prioritize sparsity/exploitation of data patterns (Akida via neurons; ECS-DoT via memory optimizations) for 10–100x energy savings over general-purpose chips.
• Scalability and Integration: Modular for multi-chip or application-specific variants; support standard ML workflows (e.g., TensorFlow) and dev tools for rapid deployment.
• Target Markets: Overlap in IoT, wearables, drones, and industrial uses, addressing the growing edge AI market (projected >$20B by 2028).

Differences

• Core Philosophy: Akida's neuromorphic approach provides brain-like adaptability and on-chip learning, ideal for dynamic, sparse environments (e.g., event-driven sensors). ECS-DoT's RISC-V foundation delivers predictable, high-throughput performance in multimodal tasks, with superior benchmark results against incumbents like STMicroelectronics or Syntiant.
• Efficiency Metrics: ECS-DoT claims quantifiable edges (e.g., 287x faster in anomaly detection), positioning it for ultra-constrained apps like extended drone flights. Akida shines in qualitative sparsity gains but lacks direct head-to-head benchmarks here.
• Maturity and Ecosystem: Akida is more established with commercial products and broader partnerships, suiting immediate prototyping. ECS-DoT, as a 2025 newcomer, leverages Nanoveu's display tech synergies (e.g., 3D imaging) but focuses on rapid OEM adoption via distributors like Arrow.
• Innovation Trade-offs: Akida enables unique features like incremental learning for evolving models, while ECS-DoT prioritizes raw speed and thermal simplicity for "set-and-forget" deployments.

In summary, choose Akida for adaptive, brain-inspired AI in vision-heavy or learning-centric apps; opt for ECS-DoT for energy-critical, multimodal inference where benchmarks matter most. Both advance edge AI, but ECS-DoT's recent benchmarks suggest it could disrupt in power-sensitive niches.

Fact Finder's response from over on the crapper..........

Hi Hop
There are some serious flaws in Grok's reasoning:

1. AKIDA Pico does not use a CPU just like all AKIDA 2.0 variants it is a standalone processor.
2. AKIDA Pico can be made up of one node or two nodes of AKIDA 2.0 neural fabric which can be expanded to 256 nodes with 131 TOPS.
3. Each node of AKIDA 2.0 is made up of four NPU with one of the four NPU configured to process TENNS,
4. AKIDA Pico therefore can run TENNS State Space Models for such things as Key Word Spotting and Noise cancellation.
5. All AKIDA technologies have the ability to process in parallel a range of inputs and fuse them to produce the desired action.
6. All AKIDA technologies only use as many nodes or NPU's as needed to process any given event.

You may remember that in the published paper introducing AERO Peter van der Made, Anil Mankar, Adam Osserian & Co used AKD1000 to process the 20 Gas Data Set each set made up of 10 different gases with State of the ART efficiency. What stood out was that this achievement involved AKD1000 only using 300 neurons leaving the majority of the neural fabric free to process other inputs.

The bottom line is that if you set up AKIDA Pico and AKIDA 2.0 to detect and take action on the same event they would both only use the same number of NPU and thus the same amount of energy. This means that if most of the time you are only detecting a single simple event, say a light being turned on or off your could use AKIDA Pico but if at much less frequent intervals some other very complex event occurs that you also want to monitor and react to you could use AKIDA 2.0 as it could do the one node processing for lengthy periods but spring into action when the major event occurs.

7. The other think which Grok misses is that all AKIDA IP can be embedded in RISC-V architecture to offload the Ai function as has been done by Frontgrade Gaisler, Andes and SiFive. This one fact puts Nanoveu's chip in a very difficult position in a terrestrial market already dominated by Andes and Si Five and in a very precarious position in the Radhard market where Frontgrade Gaisler plays.

My opinion only DYOR

Fact Finder

 

[TECH]

I feel very comfortable with this arrangement, oh, just one question.... who approached who and the big question, is why?

I will leave that for you to ponder over Einstein's

......

 

[manny100]

The question is - In what ways is the 'new' AKIDA1500 superior to the 2000 if at all?

 

[Space Cadet]

for

I feel very comfortable with this arrangement, oh, just one question.... who approached who and the big question, is why?

I will leave that for you to ponder over Einstein's

......

Interesting,

https://www.archivemarketresearch.c...ity-contract-boosting-national-security-54598

SC

 

[Diogenese]

The question is - In what ways is the 'new' AKIDA1500 superior to the 2000 if at all?

Hi Manny,

My guess is that it is adapted to handle TENNs (using the MAC-Lite circuits*), and can do regression.

* DAM**

 

[IloveLamp]

#fraunhoferiis #spikingneuralnetworks #ki #ai #glasfaser #ftte | Fraunhofer IIS

Ein gutes Ende braucht einen guten Anfang. 🎯 Im Oktober ist der Startschuss für »SpikeHERO« gefallen. Ein europäisches Projekt, das die Signalqualität von Glasfasernetzen verbessern soll, indem auftretende Störungen von KI-Chips auf Basis von Spiking Neural Networks ausgeglichen werden. Vier...

www.linkedin.com

Spiking Neural Networks

Spiking Neural Networks steigern die Effizienz Künstlicher Intelligenz durch eine vom Gehirn inspirierte Datenverarbeitung.

www.iis.fraunhofer.de

 

[Tothemoon24]

​

​

BrainChip Launches AKD1500 Edge AI Co-Processor for Low-Power Devices​

November 5, 2025BY QUANTUM NEWS

Forget cloud reliance – the future of artificial intelligence is heading straight to your devices, and BrainChip is powering that shift. The company today unveiled the AKD1500, a groundbreaking co-processor that brings advanced AI capabilities to low-power edge devices like wearables and smart sensors. Achieving an impressive 800 giga operations per second while consuming under 300 milliwatts, the AKD1500 dramatically improves performance and efficiency – a critical step towards truly ubiquitous AI in everything from healthcare and industrial automation to everyday consumer electronics. This isn’t just about faster processing; it’s about enabling adaptive learning and intelligent decision-making directly on the device, unlocking a new era of responsive and private AI experiences.

AKD1500 Performance and Efficiency​

BrainChip’s newly unveiled AKD1500 Edge AI co-processor is poised to redefine efficiency in edge computing, delivering a remarkable 800 giga operations per second (GOPS) while consuming under 300 milliwatts of power. This performance benchmark is particularly crucial for battery-powered devices and thermally constrained environments like wearables, smart sensors, and even defense applications – evidenced by early integrations with companies like Parsons, Bascom Hunter and Onsor Technologies. Unlike traditional AI accelerators reliant on cloud-based training, the AKD1500 leverages BrainChip’s Akida neuromorphic architecture to enable on-chip learning, a key differentiator. Furthermore, compatibility with x86, ARM, and RISC-V platforms through PCIe or serial interfaces, coupled with BrainChip’s MetaTF™ software suite for streamlined TensorFlow/KERAS model deployment, promises rapid adoption and reduced development costs for a wide range of AI-powered solutions.

Seamless Integration and Applications​

BrainChip’s newly launched AKD1500 Edge AI co-processor is designed for seamless integration into existing systems, a key factor for rapid deployment across diverse applications. The chip readily connects with x86, ARM, and RISC-V host processors via PCIe or serial interfaces, avoiding the need for complete system overhauls—a significant advantage for upgrading defense, industrial, and enterprise setups. This flexibility extends to embedded microcontrollers, enabling AI capabilities in healthcare, wearables, and consumer electronics. Supported by BrainChip’s MetaTF™ software suite—which streamlines model conversion from standard TensorFlow/KERAS formats—and boasting on-chip learning capabilities, the AKD1500 differentiates itself from cloud-dependent AI accelerators. Already designed into solutions with companies like Parsons, Bascom Hunter, and Onsor Technologies, the AKD1500 achieves 800 GOPS while consuming under 300 milliwatts, making it ideal for power and thermally constrained environments.

Software and On-Chip Learning​

BrainChip’s newly unveiled AKD1500 Edge AI co-processor represents a significant advancement in on-chip learning capabilities for edge devices. Unlike traditional AI accelerators dependent on cloud-based training, the AKD1500 leverages BrainChip’s Akida neuromorphic architecture to facilitate adaptive learning directly on the chip itself. This is enabled by the MetaTF™ software development tools, which streamline the conversion and deployment of standard TensorFlow/Keras models. Achieving 800 giga operations per second (GOPS) while consuming under 300 milliwatts, the AKD1500 offers exceptional power efficiency, making it ideal for battery-powered wearables, smart sensors, and thermally constrained environments. Furthermore, its compatibility with x86, ARM, and RISC-V platforms via PCIe or Serial interfaces allows for seamless integration and upgrades within existing systems – from industrial and defense applications to healthcare and consumer electronics – without requiring a complete redesign.

BrainChip Launches AKD1500 Edge AI Co-Processor For Low-Power Devices

BrainChip’s new AKD1500 co-processor delivers a significant leap in edge AI performance with ultra-low power consumption, enabling advanced AI capabilities in battery-powered devices and thermally-constrained environments. This innovative chip allows for independent AI upgrades to existing...

quantumzeitgeist.com

 

[Rach2512]

GF's innovations in power and wireless connectivity for AI and quantum computing. | GlobalFoundries posted on the topic | LinkedIn

Efficient, reliable power delivery and high-bandwidth connectivity solutions are critical factors to enable the necessary infrastructure to fuel the AI-revolution— key themes elevated at this year's #GTS2025. Hear from GF Chief Business Officer Mike Hogan on how GF is positioned to solve...

www.linkedin.com

 

[manny100]

Hi Manny,

My guess is that it is adapted to handle TENNs (using the MAC-Lite circuits*), and can do regression.

* DAM**

Hi Dio, I asked Chat gpt5. The problem with the chat boxes is that you pretty much have to know the 'guts' of the answer before you ask. The chat box then gives a useful summary you can use.
When you do not have a fair idea of the answer before you cant be sure if its right unless you check thoroughly.
I asked and here is an extract from what.
" Direct answer: The AKD1500 is more advanced in raw performance and integration flexibility, while Akida Gen2 (IP platform) is broader in scope, designed as a licensable architecture for embedding into many SoCs. In terms of chip performance, the AKD1500 is the stronger, mission‑ready device. In terms of design flexibility and ecosystem reach, Akida Gen2 is more versatile."
Its a general statement and probably not much different to what we might have suspected/guessed given its for defense and health
It goes into more detail but not worth reproducing here.
I take it with a great deal of suspicion as apart from what we and chat box know from the recent releases not much is known about the new 1500. Unless there are comprehensive spec sheets out there - which i doubt - why tell the world 9 months out from delivery?
There has to be significant changes or they just would have put the initial 'mask' with minor changes out to production.

 

[GStocks123]

Anyone have access to the full text? Recent Valeo publication on LiDAR with SNN

Indirect Time of Flight Near Field LiDAR Depth Correction Using Spiking Neural Networks | Request PDF

Request PDF | Indirect Time of Flight Near Field LiDAR Depth Correction Using Spiking Neural Networks | Spiking Neural Networks (SNN) is a machine learning model inspired by the spiking nature of biological human brain neurons. These neural models... | Find, read and cite all the research you...

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