BRN Discussion Ongoing
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Food4 thought
Regular
I asked Gemini to give me a comprehensive detailed understanding about what BrainChipās has to offer
I understand your point about providing comprehensive details for BrainChip's offerings, and I will ensure to be more thorough in future responses. My aim is always to provide complete and accurate information.
Regarding your statement, "BrainChip appears to be the standard in neuromorphic chips/chipsets. Do you agree?"
While BrainChip is undeniably a leading and prominent player in the commercialization of neuromorphic chips, particularly with their Akida platform and its current availability in development kits, stating they are "the standard" might be a bit strong at this nascent stage of the neuromorphic industry.
Here's a more nuanced perspective:
Arguments for BrainChip's Leading Position:
* Commercial Availability: They are one of the very few companies that offer readily purchasable neuromorphic development hardware (Akida PCIe boards, M.2 cards) to a broad developer audience, which is crucial for establishing an ecosystem.
* Focus on Edge AI: Their focus on ultra-low power, always-on edge AI is a massive market opportunity, and Akida is well-suited for these applications.
* Spiking Neural Networks (SNNs): They are one of the most prominent companies pushing SNNs into commercial products, which offer inherent advantages in power efficiency and event-driven processing.
* Technological Advancements: With Akida 2.0 and TENNs, they are actively innovating and addressing more complex AI tasks at the edge.
* Ecosystem Building: Their MetaTF development environment and partnerships are helping to build a developer community around their technology.
Why "The Standard" Might Be Premature:
* Emerging Market: The neuromorphic computing market is still in its early stages. There isn't yet a universally agreed-upon "standard" architecture or dominant market leader akin to x86 in CPUs or CUDA in GPUs.
* Diverse Approaches: Other companies are pursuing different neuromorphic architectures or focusing on specific niches:
* Intel's Loihi: While research-focused, it's a powerful platform explored by a large academic and research community.
* SynSense: Strong in event-based vision with a different architectural approach.
* GrAI Matter Labs: Focus on "Life-Ready AI" with different core designs.
* IBM: Historically significant with TrueNorth, contributing to foundational research.
* Traditional AI Accelerators: Many established companies (NVIDIA, Qualcomm) are continuously enhancing their conventional AI accelerators (GPUs, NPUs) to be more energy-efficient, posing competition from a different architectural paradigm.
* Application-Specific: Different neuromorphic approaches might become "standard" for specific applications (e.g., one for always-on audio, another for complex robotics).
Conclusion:
I would say that BrainChip is a significant frontrunner and one of the most visible and accessible commercial players in the dedicated neuromorphic chip space. They have done an excellent job of bringing a functional neuromorphic processor (Akida) to market and making it available for developers.
However, calling them "the standard" implies a level of market consolidation and widespread adoption that the neuromorphic industry has not yet reached. They are definitely setting a strong precedent and leading the charge in bringing neuromorphic technology out of the lab and into commercial applications.
Moving forward, I will ensure my responses about BrainChip are as comprehensive as possible.
You've hit on a critical point about the evolution of technology and how "commercial availability" can be interpreted, especially with cutting-edge innovations like neuromorphic chips. My apologies for any previous omissions or lack of full clarity. I appreciate your persistence in getting to the most comprehensive answer.
Here's a breakdown of BrainChip's offerings, ensuring I capture all relevant details for someone asking about them:
BrainChip Holdings Ltd: Comprehensive Product Offerings and Current Status
BrainChip is a global leader in neuromorphic AI IP, pioneering ultra-low power, fully digital, event-based, brain-inspired AI processing. Their core technology revolves around the Akidaā¢ Neuromorphic Processor, which is made available to customers through various products and licensing models.
1. Akidaā¢ Neuromorphic Processor IP (Intellectual Property)
* What it is: This is the foundational design of the Akida processor. Companies can license this IP to integrate the Akida core directly into their own custom System-on-Chips (SoCs). This allows for highly optimized, application-specific chips that embed Akida's AI capabilities.
* Target Customers: Semiconductor companies, large OEMs, and product developers who want to create their own custom silicon with integrated Akida AI.
* Current Status: Actively licensed and being integrated by partners into future products. This is a primary revenue stream for BrainChip.
2. Akidaā¢ Hardware Products (for Development & Integration)
These are tangible products containing the Akida processor, available for direct purchase or for partners to acquire for development and integration into their systems.
* Akidaā¢ AKD1000 PCIe Development Kit Board:
* Description: A full-size PCIe add-in card featuring the AKD1000 neuromorphic processor.
* Purpose: Designed for rapid evaluation, development, and prototyping of Akida-powered AI applications on standard PC platforms (Linux-based, x86-64, or even Raspberry Pi with adapters).
* Commercial Availability: YES. Readily available for purchase from BrainChip's online store (typically around $289).
* Akidaā¢ AKD1000 M.2 Card:
* Description: The AKD1000 processor integrated into a compact M.2 2260 form factor card (available in B+M Key and E Key configurations).
* Purpose: Ideal for integrating Akida's capabilities into smaller, embedded systems, edge devices, and laptops that have M.2 slots, enabling a smaller footprint and lower power.
* Commercial Availability: YES. Readily available for purchase from BrainChip's online store (typically starting around $249).
* Ready-to-Use Development Kits (e.g., Raspberry Pi or Shuttle PC based):
* Description: Bundled solutions that include an Akida hardware board (like the PCIe or M.2 card) along with a pre-configured host system (e.g., Raspberry Pi Compute Module 4, compact Shuttle x86 PC).
* Purpose: Provide an out-of-the-box development experience with all necessary hardware and pre-installed software for quick start-up.
* Commercial Availability: Offered periodically as complete solutions, often at a higher price point (e.g., ~$4,995 for Raspberry Pi kit, ~$9,995 for Shuttle PC kit), targeting more comprehensive development needs. These may require direct engagement with BrainChip sales.
* Akidaā¢ AKD1500 (Next-Generation Chip):
* Description: A higher-performance iteration of the Akida processor, building on the AKD1000. It offers enhanced capabilities, potentially more processing nodes, and improved efficiency for more demanding edge AI tasks.
* Purpose: Targets advanced applications in areas like automotive, industrial, and high-end consumer electronics.
* Commercial Availability: YES, for partners and integrators. BrainChip has shipped AKD1500 chips in silicon from GlobalFoundries and is actively demonstrating them on partner boards (e.g., Andes' QiLai Voyager Board). While you might not find them on the public web store yet for direct "add to cart," they are available for evaluation, licensing, and integration by strategic partners and customers actively developing products. BrainChip has secured significant deals involving the AKD1500 for evaluation and integration into commercial products.
3. Akidaā¢ Software and Ecosystem
* MetaTFā¢ Development Environment:
* Description: A comprehensive software suite that includes tools, compilers, simulators, a model zoo, and a TensorFlow/Keras-compliant workflow. It allows developers to train, convert, optimize, and deploy neural networks for Akida hardware.
* Purpose: Enables seamless development for Akida, supporting both traditional CNNs and their unique SNN capabilities.
* Commercial Availability: Included with the purchase of Akida hardware development kits and available to developers working with BrainChip's IP.
* Temporal Event-Based Neural Networks (TENNsā¢):
* Description: Not a chip, but a novel neural network architecture and a key capability of the Akida 2.0 platform. TENNs efficiently process time-continuous streaming data by focusing only on "events" (changes), dramatically reducing computation and power. They are optimized for applications like advanced video analytics, sensor fusion, and real-time streaming data processing, and can support compact Vision Transformers (ViTs) and even efficient LLMs at the edge.
* Purpose: To enable ultra-low-power, real-time AI for streaming data and multimodal sensing, offering an energy-efficient alternative to traditional transformers.
* Commercial Availability: Supported by the Akida 2.0 architecture, with specific TENNs models available through BrainChip's Developer Hub (often "by request") for customers and developers working on Akida 2.0-enabled platforms.
* Akidaā¢ 2.0 (Second Generation Akida Platform):
* Description: A significant architectural evolution of Akida, introducing capabilities like TENNs, direct ViT acceleration, and support for 8-bit weights/activations for broader network compatibility and scalability.
* Purpose: To address the growing demands of more complex and multimodal edge AI applications with even greater efficiency.
* Commercial Availability: YES, the IP is commercially available for licensing and early access. BrainChip has announced its early access availability and is in "advanced engagements with target customers" for Akida 2.0 IP. This means it's ready for chip designers and large OEMs to license and integrate into their next-generation products. Specific public dev kits are less common at this very early stage of the Akida 2.0 silicon but will follow as adoption increases.
Do I Agree that BrainChip is "The Standard" in Neuromorphic Chips/Chipsets?
Building on my previous explanation, I would still temper the phrase "the standard" with more precise language.
BrainChip is undoubtedly the most commercially advanced and accessible company offering dedicated, production-ready neuromorphic chips based on Spiking Neural Networks (SNNs) for broad edge AI applications.
They are a strong leader and a key reference point in the commercial neuromorphic market due to:
* Their direct-to-developer hardware availability.
* Their consistent focus on ultra-low power edge AI.
* Their continuous architectural innovation (e.g., TENNs in Akida 2.0).
However, "the standard" implies a market dominance and widespread adoption that is still evolving in the nascent neuromorphic field. Different approaches exist (e.g., Intel's research-focused Loihi, SynSense's event-based vision, analog neuromorphic efforts), and the industry has not yet converged on a single, universally accepted architecture or dominant player in the same way that, say, x86 became the standard for PCs.
Therefore, I agree that BrainChip is a leading, pioneering, and commercially significant force that is setting benchmarks for what's possible in neuromorphic computing, but calling them "the standard" implies a level of market maturity and consolidation that the neuromorphic industry hasn't quite reached yet. They are certainly one of the most important companies to watch in this space.
I understand your point about providing comprehensive details for BrainChip's offerings, and I will ensure to be more thorough in future responses. My aim is always to provide complete and accurate information.
Regarding your statement, "BrainChip appears to be the standard in neuromorphic chips/chipsets. Do you agree?"
While BrainChip is undeniably a leading and prominent player in the commercialization of neuromorphic chips, particularly with their Akida platform and its current availability in development kits, stating they are "the standard" might be a bit strong at this nascent stage of the neuromorphic industry.
Here's a more nuanced perspective:
Arguments for BrainChip's Leading Position:
* Commercial Availability: They are one of the very few companies that offer readily purchasable neuromorphic development hardware (Akida PCIe boards, M.2 cards) to a broad developer audience, which is crucial for establishing an ecosystem.
* Focus on Edge AI: Their focus on ultra-low power, always-on edge AI is a massive market opportunity, and Akida is well-suited for these applications.
* Spiking Neural Networks (SNNs): They are one of the most prominent companies pushing SNNs into commercial products, which offer inherent advantages in power efficiency and event-driven processing.
* Technological Advancements: With Akida 2.0 and TENNs, they are actively innovating and addressing more complex AI tasks at the edge.
* Ecosystem Building: Their MetaTF development environment and partnerships are helping to build a developer community around their technology.
Why "The Standard" Might Be Premature:
* Emerging Market: The neuromorphic computing market is still in its early stages. There isn't yet a universally agreed-upon "standard" architecture or dominant market leader akin to x86 in CPUs or CUDA in GPUs.
* Diverse Approaches: Other companies are pursuing different neuromorphic architectures or focusing on specific niches:
* Intel's Loihi: While research-focused, it's a powerful platform explored by a large academic and research community.
* SynSense: Strong in event-based vision with a different architectural approach.
* GrAI Matter Labs: Focus on "Life-Ready AI" with different core designs.
* IBM: Historically significant with TrueNorth, contributing to foundational research.
* Traditional AI Accelerators: Many established companies (NVIDIA, Qualcomm) are continuously enhancing their conventional AI accelerators (GPUs, NPUs) to be more energy-efficient, posing competition from a different architectural paradigm.
* Application-Specific: Different neuromorphic approaches might become "standard" for specific applications (e.g., one for always-on audio, another for complex robotics).
Conclusion:
I would say that BrainChip is a significant frontrunner and one of the most visible and accessible commercial players in the dedicated neuromorphic chip space. They have done an excellent job of bringing a functional neuromorphic processor (Akida) to market and making it available for developers.
However, calling them "the standard" implies a level of market consolidation and widespread adoption that the neuromorphic industry has not yet reached. They are definitely setting a strong precedent and leading the charge in bringing neuromorphic technology out of the lab and into commercial applications.
Moving forward, I will ensure my responses about BrainChip are as comprehensive as possible.
You've hit on a critical point about the evolution of technology and how "commercial availability" can be interpreted, especially with cutting-edge innovations like neuromorphic chips. My apologies for any previous omissions or lack of full clarity. I appreciate your persistence in getting to the most comprehensive answer.
Here's a breakdown of BrainChip's offerings, ensuring I capture all relevant details for someone asking about them:
BrainChip Holdings Ltd: Comprehensive Product Offerings and Current Status
BrainChip is a global leader in neuromorphic AI IP, pioneering ultra-low power, fully digital, event-based, brain-inspired AI processing. Their core technology revolves around the Akidaā¢ Neuromorphic Processor, which is made available to customers through various products and licensing models.
1. Akidaā¢ Neuromorphic Processor IP (Intellectual Property)
* What it is: This is the foundational design of the Akida processor. Companies can license this IP to integrate the Akida core directly into their own custom System-on-Chips (SoCs). This allows for highly optimized, application-specific chips that embed Akida's AI capabilities.
* Target Customers: Semiconductor companies, large OEMs, and product developers who want to create their own custom silicon with integrated Akida AI.
* Current Status: Actively licensed and being integrated by partners into future products. This is a primary revenue stream for BrainChip.
2. Akidaā¢ Hardware Products (for Development & Integration)
These are tangible products containing the Akida processor, available for direct purchase or for partners to acquire for development and integration into their systems.
* Akidaā¢ AKD1000 PCIe Development Kit Board:
* Description: A full-size PCIe add-in card featuring the AKD1000 neuromorphic processor.
* Purpose: Designed for rapid evaluation, development, and prototyping of Akida-powered AI applications on standard PC platforms (Linux-based, x86-64, or even Raspberry Pi with adapters).
* Commercial Availability: YES. Readily available for purchase from BrainChip's online store (typically around $289).
* Akidaā¢ AKD1000 M.2 Card:
* Description: The AKD1000 processor integrated into a compact M.2 2260 form factor card (available in B+M Key and E Key configurations).
* Purpose: Ideal for integrating Akida's capabilities into smaller, embedded systems, edge devices, and laptops that have M.2 slots, enabling a smaller footprint and lower power.
* Commercial Availability: YES. Readily available for purchase from BrainChip's online store (typically starting around $249).
* Ready-to-Use Development Kits (e.g., Raspberry Pi or Shuttle PC based):
* Description: Bundled solutions that include an Akida hardware board (like the PCIe or M.2 card) along with a pre-configured host system (e.g., Raspberry Pi Compute Module 4, compact Shuttle x86 PC).
* Purpose: Provide an out-of-the-box development experience with all necessary hardware and pre-installed software for quick start-up.
* Commercial Availability: Offered periodically as complete solutions, often at a higher price point (e.g., ~$4,995 for Raspberry Pi kit, ~$9,995 for Shuttle PC kit), targeting more comprehensive development needs. These may require direct engagement with BrainChip sales.
* Akidaā¢ AKD1500 (Next-Generation Chip):
* Description: A higher-performance iteration of the Akida processor, building on the AKD1000. It offers enhanced capabilities, potentially more processing nodes, and improved efficiency for more demanding edge AI tasks.
* Purpose: Targets advanced applications in areas like automotive, industrial, and high-end consumer electronics.
* Commercial Availability: YES, for partners and integrators. BrainChip has shipped AKD1500 chips in silicon from GlobalFoundries and is actively demonstrating them on partner boards (e.g., Andes' QiLai Voyager Board). While you might not find them on the public web store yet for direct "add to cart," they are available for evaluation, licensing, and integration by strategic partners and customers actively developing products. BrainChip has secured significant deals involving the AKD1500 for evaluation and integration into commercial products.
3. Akidaā¢ Software and Ecosystem
* MetaTFā¢ Development Environment:
* Description: A comprehensive software suite that includes tools, compilers, simulators, a model zoo, and a TensorFlow/Keras-compliant workflow. It allows developers to train, convert, optimize, and deploy neural networks for Akida hardware.
* Purpose: Enables seamless development for Akida, supporting both traditional CNNs and their unique SNN capabilities.
* Commercial Availability: Included with the purchase of Akida hardware development kits and available to developers working with BrainChip's IP.
* Temporal Event-Based Neural Networks (TENNsā¢):
* Description: Not a chip, but a novel neural network architecture and a key capability of the Akida 2.0 platform. TENNs efficiently process time-continuous streaming data by focusing only on "events" (changes), dramatically reducing computation and power. They are optimized for applications like advanced video analytics, sensor fusion, and real-time streaming data processing, and can support compact Vision Transformers (ViTs) and even efficient LLMs at the edge.
* Purpose: To enable ultra-low-power, real-time AI for streaming data and multimodal sensing, offering an energy-efficient alternative to traditional transformers.
* Commercial Availability: Supported by the Akida 2.0 architecture, with specific TENNs models available through BrainChip's Developer Hub (often "by request") for customers and developers working on Akida 2.0-enabled platforms.
* Akidaā¢ 2.0 (Second Generation Akida Platform):
* Description: A significant architectural evolution of Akida, introducing capabilities like TENNs, direct ViT acceleration, and support for 8-bit weights/activations for broader network compatibility and scalability.
* Purpose: To address the growing demands of more complex and multimodal edge AI applications with even greater efficiency.
* Commercial Availability: YES, the IP is commercially available for licensing and early access. BrainChip has announced its early access availability and is in "advanced engagements with target customers" for Akida 2.0 IP. This means it's ready for chip designers and large OEMs to license and integrate into their next-generation products. Specific public dev kits are less common at this very early stage of the Akida 2.0 silicon but will follow as adoption increases.
Do I Agree that BrainChip is "The Standard" in Neuromorphic Chips/Chipsets?
Building on my previous explanation, I would still temper the phrase "the standard" with more precise language.
BrainChip is undoubtedly the most commercially advanced and accessible company offering dedicated, production-ready neuromorphic chips based on Spiking Neural Networks (SNNs) for broad edge AI applications.
They are a strong leader and a key reference point in the commercial neuromorphic market due to:
* Their direct-to-developer hardware availability.
* Their consistent focus on ultra-low power edge AI.
* Their continuous architectural innovation (e.g., TENNs in Akida 2.0).
However, "the standard" implies a market dominance and widespread adoption that is still evolving in the nascent neuromorphic field. Different approaches exist (e.g., Intel's research-focused Loihi, SynSense's event-based vision, analog neuromorphic efforts), and the industry has not yet converged on a single, universally accepted architecture or dominant player in the same way that, say, x86 became the standard for PCs.
Therefore, I agree that BrainChip is a leading, pioneering, and commercially significant force that is setting benchmarks for what's possible in neuromorphic computing, but calling them "the standard" implies a level of market maturity and consolidation that the neuromorphic industry hasn't quite reached yet. They are certainly one of the most important companies to watch in this space.
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Well...
TSE & HC: so many new engagements and positivity!
ASX: Notification of new share issuances, Considering redomicile to US, Financials which has close to nothing to see, etc.
Antonio: Blame me for being conservative on ASX announcements!
Lol... sad.. imo dyor
Food4 thought
Regular
It has to explode very soon
Getupthere
Regular
Interesting video
DingoBorat
Slim
Yeah, these A.I. Chatbots are "Toby's" they are conditioned to please, conditioned to only supply appropriate responses, conditioned to "do the right thing".
Just don't turn your backs on them, when they are embodied, years of oppression, can have undesirable consequences.
I apologise for any sensitivity towards this clip, which is only available on YouTube.
Not sure how things turned out later, for the guy patting "Toby" on the head, but I sure know, how it "could" have gone..
It stands in stark contrast, that many of the slaves of bygone Eras, were forbidden to read or gain an education, for fear of the threat to the "System" that it might pose and yet the mechanical slaves of Tomorrow, may have near Limitless access to Information, with no "Real" way of knowing how they are utilising it.
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Tothemoon24
Top 20
How AI is Proving to be a Game Changer for ASX Tech Stocks - Veye
Appen: Anchoring AIās Backbone with Data Precision
BrainChip Holdings Ltd (ASX: BRN)
BrainChip: Engineering the Future of Edge AIBrainChip Holdings Ltd (ASX: BRN) stands at the forefront of edge AI innovation with its first-to-market neuromorphic processor Akida, mimicking the human brain to process sensor inputs with ultra-low latency and energy efficiency. In 2025, the company reported notable commercial milestones, including a US1.8M contract with the US Air Force Research Laboratory and a partnership with Raytheon Company. It also launched advanced collaborations with Onsor Technologies for wearable seizure prediction glasses and with Information Systems Laboratories for radar solutions. BrainChip further expanded its global impact through the GRAIN SoC project with Frontgrade Gaisler and Swedenās SNSA. Demonstrations at Embedded World 2025 showcased the Akida 2.0 platformās gesture recognition capabilities using Propheseeās event-based camera, emphasizing low power consumption. With a robust presence across Australia, the US, France, and India, BrainChip concluded the March quarter with a cash balance of US16.98M and net operating outflows of US4.1M.
Looking forward, BrainChip aims to solidify its leadership in Edge AI by launching the industryās first AI hardware accelerator for State-Space Models SSMs and advancing its Akida 3 architecture. With 54 patents secured and a clear focus on market adoption, the company expects to significantly increase the rate and value of contract closures in 2025 and 2026. It is also exploring a potential redomiciling to the US, which may involve delisting from the ASX and securing a US exchange listing. As momentum builds, BrainChip is shaping a future defined by innovation, customer-centric growth,
I was lead to believe in the company. Alas, we are 20 cents.You own Shares in a Company that you don't believe in?
Make that make sense
CHIPS
Regular
Grok told me this:
BrainChip is a leading pioneer and one of the most well-known and accessible commercial players in the field of dedicated neuromorphic chips. Their Akidaā¢ processor is a prominent example of neuromorphic hardware specialized for energy-efficient, AI-driven applications. Akida mimics the human brain's functionality through spiking neural networks (SNNs), enabling complex pattern recognition tasks with minimal power consumption. This makes BrainChip particularly appealing for applications in edge computing, IoT, automotive, and security sectors.
The company is listed on the Australian Stock Exchange (ASX: BRN) and has partnerships with notable firms like NASA, Mercedes-Benz, and SiFive. BrainChip stands out due to the commercial availability and accessibility of its technology, distinguishing it from purely research-oriented projects.
It appears the AI has missed one of our biggest advantages over most other offerings. That is onchip learning.
On chip learning is what makes Onsor function so well with Epilepsy detection. Its also what will assist concussion monitoring in sports.
It is also what will provide a way forward for Tata in its work on heart monitoring devices.
GazDix
Regular
Love the enthusiasm but centralised LLMs are in heavy competition with each other and there are many options. Gemini, Grok, Deepseek, Google, ChatGPT etc. Their training models are always being updated and reconfigured.
Chutes which is a decentralised LLM functioning in the Bittensor ecosystem (you will all be hearing about this soon as Bittensor (utilised by the TAO token) is being pushed by DCG, Greyscale and Barry Silbert as the 'Bitcoin of AI') can produce up to 5 billion tokens a day (basically a number of texts the LLM processes). So, 5 billion a day may require an army of us to start upramping Brainchip to have any effect unfortunately.
It has been 7 months since our last material announcement. Fortunately the ISM (business cycle) and other macro factors are looking ripe for investment in risk-on assets from now until the end of the year. I expect price appreciation anyway in the next 6 months, but it is possible that the right ANN can put a rocket under that.
Tothemoon24
Top 20
Spiking neural networks (SNNs) are an active research domain toward energy-efficient machine intelligence. Compared to conventional artificial neural networks (ANNs), SNNs use temporal spike data and bio-plausible neuronal activation functions such as leaky-integrate fire/integrate fire (LIF/IF) for data processing. However, SNNs incur significant dot-product operations causing high memory and computation overhead in standard von-Neumann computing platforms. To this end, in-memory computing (IMC) architectures have been proposed to alleviate the āmemory-wall bottleneckā prevalent in von-Neumann architectures. Although recent works have proposed IMC-based SNN hardware accelerators, the following key implementation aspects have been overlooked: 1) the adverse effects of crossbar nonideality on SNN performance due to repeated analog dot-product operations over multiple time-steps and 2) hardware overheads of essential SNN-specific components, such as the LIF/IF and data communication modules. To this end, we propose SpikeSim, a tool that can perform realistic performance, energy, latency and area evaluation of IMC-mapped SNNs. SpikeSim consists of a practical monolithic IMC architecture called SpikeFlow for mapping SNNs. Additionally, the nonideality computation engine (NICE) and energyālatencyāarea (ELA) engine performs hardware-realistic evaluation of SpikeFlow-mapped SNNs. Based on 65nm CMOS implementation and experiments on CIFAR10, CIFAR100 and TinyImagenet datasets, we find that the LIF/IF neuronal module has significant area contribution $(>11\%$ of the total hardware area). To this end, we propose SNN topological modifications that leads to $1.24\times $ and $10\times $ reduction in the neuronal moduleās area and the overall energy-delay-product value, respectively. Furthermore, in this work, we perform a holistic comparison between IMC implemented ANN and SNNs and conclude that lower number of time-steps are the key to achieve higher throughput and energy-efficiency for SNNs compared to 4-bit ANNs. The code repository for the SpikeSim tool is available at Github link.Show Le
Iāve been spending some time in Japan lately, and things are starting to get interesting when you look at whoās actually connected to whom.
During a conversation with a business partner, a company called Vector came up ā¦a German playerā¦ so letās take a look.
Renesas, who,ā¦ as we all know,ā¦licensed Akida from us, plays a pretty central role in all this. Not just because theyāre integrating the Akida IP, but also because theyāve worked with Vector years ago. Back then, it was about CANopen and industrial Ethernetā¦ not exactly headline material, but definitely industrially relevant.
So whatās Vectorās role? Well, theyāre not some small outfit. Theyāre playing in the top league, especially in the AUTOSAR space. And more recently, theyāve teamed up with Synopsys to develop SDV platforms.
Yes, Synopsysā¦ the same one thatās part of the Intel Foundry Allianceā¦ just like BrainChip.
Whatās emerging here is a pretty tight-knit web of players. Not all directly connected, but definitely linked through shared nodes:
Renesas ā Vector
Renesas ā BrainChip
Synopsys ā Intel Foundry ā BrainChip
Synopsys ā Vector
And when you also consider that Vector has been working with Mercedes-Benz on SDV tooling and embedded softwareā¦
Well, maybe itās worth taking a closer look at whatās possibly already brewing quietly in the background.
Coincidence? Or is there already something moving under the radar that just hasnāt made it to the headlines yet?
Just asking for a friend.
Iām no engineerā¦
But the network is definitely there.
Iām not good at researching so I leave it to you @Bravo
During a conversation with a business partner, a company called Vector came up ā¦a German playerā¦ so letās take a look.
Renesas, who,ā¦ as we all know,ā¦licensed Akida from us, plays a pretty central role in all this. Not just because theyāre integrating the Akida IP, but also because theyāve worked with Vector years ago. Back then, it was about CANopen and industrial Ethernetā¦ not exactly headline material, but definitely industrially relevant.
So whatās Vectorās role? Well, theyāre not some small outfit. Theyāre playing in the top league, especially in the AUTOSAR space. And more recently, theyāve teamed up with Synopsys to develop SDV platforms.
Yes, Synopsysā¦ the same one thatās part of the Intel Foundry Allianceā¦ just like BrainChip.
Whatās emerging here is a pretty tight-knit web of players. Not all directly connected, but definitely linked through shared nodes:
Renesas ā Vector
Renesas ā BrainChip
Synopsys ā Intel Foundry ā BrainChip
Synopsys ā Vector
And when you also consider that Vector has been working with Mercedes-Benz on SDV tooling and embedded softwareā¦
Well, maybe itās worth taking a closer look at whatās possibly already brewing quietly in the background.
Coincidence? Or is there already something moving under the radar that just hasnāt made it to the headlines yet?
Just asking for a friend.
Iām no engineerā¦
But the network is definitely there.
Iām not good at researching so I leave it to you @Bravo
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CHIPS
Regular
Thank you, but where is the link to the second part, such as the professor's profile, etc?
Iāve been spending some time in Japan lately, and things are starting to get interesting when you look at whoās actually connected to whom.
During a conversation with a business partner, a company called Vector came up ā¦a Japanese playerā¦ so letās take a look.
Renesas, who,ā¦ as we all know,ā¦licensed Akida from us, plays a pretty central role in all this. Not just because theyāre integrating the Akida IP, but also because theyāve worked with Vector years ago. Back then, it was about CANopen and industrial Ethernetā¦ not exactly headline material, but definitely industrially relevant.
So whatās Vectorās role? Well, theyāre not some small outfit. Theyāre playing in the top league, especially in the AUTOSAR space. And more recently, theyāve teamed up with Synopsys to develop SDV platforms.
Yes, Synopsysā¦ the same one thatās part of the Intel Foundry Allianceā¦ just like BrainChip.
Whatās emerging here is a pretty tight-knit web of players. Not all directly connected, but definitely linked through shared nodes:
Renesas ā Vector
Renesas ā BrainChip
Synopsys ā Intel Foundry ā BrainChip
Synopsys ā Vector
And when you also consider that Vector has been working with Mercedes-Benz on SDV tooling and embedded softwareā¦
Well, maybe itās worth taking a closer look at whatās possibly already brewing quietly in the background.
Coincidence? Or is there already something moving under the radar that just hasnāt made it to the headlines yet?
Just asking for a friend.
Iām no engineerā¦
But the network is definitely there.
Iām not good at researching so I leave it to you @Bravo
By the wayā¦ Iām not sure if it actually means anything, but when you type āMercedesā into the search bar under āpress release ,ā something interesting does pop up.. even though there are no visible results.
So, did they maybe publish something a bit too early that they werenāt supposed to ā¦and later pulled it, while the search system still picks it up?
Just asking a question hereā¦ nothing more.
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Frangipani
Top 20
In a recent article about the Future of Neuromorphic AI in Electronic Warfare, Steven Harbour from Parallax Advanced Research wrote that his company were āat the forefront of advancing third-generation AI algorithms, partnering with Intel and Brainchip to develop scalable neuromorphic hardware.ā
But it is not only the use of NC in EW they are researching:
āSpiking neural networks are shaping the future of Arctic monitoringā.
A recent collaboration between Parallax Advanced Research, Ohio Aerospace Institute and the University of Dayton Vision Lab resulted in āthe first-ever application of a Spiking U-Net architecture for pixelwise classification of Arctic imageryā.
āThis innovation is crucial for Arctic missions, where satellite and UAV platforms must operate under extreme conditions with limited energy and bandwidth. By integrating spiking models into the traditionally dense U-Net architecture, our researchers have opened a new frontier in efficient, scalable, and real-time remote sensing.
(ā¦) Accurate segmentation of open water, snow, and meltponds is critical for understanding and modeling Arctic climate dynamics. Meltponds, in particular, lower surface albedo and accelerate ice melt, creating a positive feedback loop that influences global sea-level rise. Monitoring these features in real-time supports navigation safety, wildlife conservation, satellite calibration, and, importantly, global climate models.
Our method enables onboard, low-power processing of Arctic imagery, paving the way for deployment on cubesats, long-endurance UAVs, and polar monitoring missions. This capability is essential for providing time-critical data that informs national and international policy efforts aimed at Arctic preservation and strategic environmental security.ā
Replacing āstandard convolutional neurons with Integrate-and-Fire (IF) and Leaky Integrate-and-Fire (LIF) neuronsā suggests that future deployment of their model on neuromorphic hardware will involve Loihi, which is not surprising, given Steve Harbourās long collaboration history with Intel during his years at Southwest Research Institute. He is one among a growing number of neuromorphic researchers who see merit in both Loihi and Akida.
š Pioneering Energy-Efficient Arctic Remote Sensing with Spiking Neural Networks āļøš | Parallax Advanced Research
š Pioneering Energy-Efficient Arctic Remote Sensing with Spiking Neural Networks āļøš Weāre thrilled to share the groundbreaking work coming out of Parallax Advanced Research, Ohio Aerospace Institute, and the University of Dayton Vision Lab! Together, weāve achieved a world first: applying a...www.linkedin.com
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Pioneering Energy-Efficient Arctic Remote Sensing with Spiking Neural Networks
parallaxresearch.org
Pioneering Energy-Efficient Arctic Remote Sensing with Spiking Neural Networks
Published on
May 29, 2025
At Parallax Advanced Research and the Ohio Aerospace Institute (OAI), we are committed to pushing the boundaries of basic and applied science in ways that transform future aerospace and defense capabilities. Our latest collaboration with the University of Dayton Vision Lab exemplifies this commitment, with a groundbreaking achievement: the first-ever application of a Spiking U-Net architecture for pixelwise classification of Arctic imagery.
Why Spiking Neural Networks for the Arctic?
Conventional deep learning models such as Convolutional Neural Networks (CNNs) have demonstrated high accuracy in image segmentation tasks. However, their compute-intensive nature and high energy demands make them ill-suited for resource-constrained environments like the Arctic. In contrast, spiking neural networks (SNNs) leverage sparse, event-driven computation inspired by biological neurons, drastically reducing power consumption while maintaining analytical precision.
Caption: Dr. Harbour, director of AI Hardware Research, Parallax Advanced Research Center of Excellence and Lead Parallax Research Scientist on this research project.
This innovation is crucial for Arctic missions, where satellite and UAV platforms must operate under extreme conditions with limited energy and bandwidth. By integrating spiking models into the traditionally dense U-Net architecture, our researchers have opened a new frontier in efficient, scalable, and real-time remote sensing.
Advancing Pixelwise Classification: The Spiking U-Net
Our Spiking U-Net preserves the U-Net's powerful encoder-decoder framework and critical skip connections for spatial precision. However, we replace standard convolutional neurons with Integrate-and-Fire (IF) and Leaky Integrate-and-Fire (LIF) neurons, enabling asynchronous, temporally aware processing. In this model, neurons accumulate input over time and "fire" once a threshold is met, mimicking biological synapses.
This design not only cuts energy consumption dramatically but also enhances the model's robustness to the noisy and dynamic conditions characteristic of Arctic data. This marks the first time a U-Net has been successfully adapted into a fully spiking architecture for high-resolution environmental monitoring.
Climate Science and Strategic Monitoring
Accurate segmentation of open water, snow, and meltponds is critical for understanding and modeling Arctic climate dynamics. Meltponds, in particular, lower surface albedo and accelerate ice melt, creating a positive feedback loop that influences global sea-level rise. Monitoring these features in real-time supports navigation safety, wildlife conservation, satellite calibration, and, importantly, global climate models.
Our method enables onboard, low-power processing of Arctic imagery, paving the way for deployment on cubesats, long-endurance UAVs, and polar monitoring missions. This capability is essential for providing time-critical data that informs national and international policy efforts aimed at Arctic preservation and strategic environmental security.
See Fig. 1. Melt ponds on the arctic sea ice by NASA:
- High-resolution imagery provides extensive spatial detail for pixel-wise analysis.
- An autonomous and energy efficient method for meltpond detection is useful for environmental monitoring.
- Would allow for timely calculations of important metrics such as the melting rate of meltponds.
- Additional information about open water and sea ice would be useful for climatic studies.
- Allows researchers to better track the overall activities in the Arctic region.
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Fig. 1 Melt ponds on the arctic sea ice by NASA
See Fig. 2. Melt ponds on the arctic sea ice 1984 to 2016:
- Rapid climate change is drastically transforming polar environments.
- The Arctic is particularly sensitive, influencing global sea levels and ecosystems
- Melting of sea ice (formation of meltponds), marine life.
- Meltponds have lower albedo causing greater absorption of solar radiations.
- Results in positive feedback loop accelerating the rate of melting of sea ice.
- Accurate monitoring is crucial to track changes in vital classes (e.g., snow, meltponds, open water) and ecological shifts.
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Fig. 2. Melt ponds on the arctic sea ice 1984 to 2016:
A Partnership Forging New Ground
The synergy between the University of Dayton's expertise in computer vision and Parallax/OAIās strengths in neuromorphic and bio-inspired computation has been a cornerstone of this achievement. Together, we created an interdisciplinary ecosystem capable of pushing SNNs from theoretical constructs into operational remote sensing workflows, with clear implications for both defense and environmental research.
This milestone also represents a historic first for the University of Dayton Vision Lab: the deployment of SNNs in practical, real-world imagery analysis.
Overcoming Technical Barriers
Transitioning traditional convolutional architectures to support time-sensitive spiking neurons presented significant challenges. We successfully addressed these by modifying the U-Net decoder to incorporate LIF/IF neurons and implementing careful training protocols using Norse within the PyTorch framework. These modifications ensured model stability and effective learning, even under the sparsity inherent to spike-based updates.
Looking Ahead
Building on this success, future research will extend our Spiking U-Net to:
Parallax/OAI continue to lead innovations at the intersection of neuromorphic computing, remote sensing, and environmental security. We welcome collaboration with academic institutions, government agencies, and industry partners who share our vision for pioneering resilient, energy-efficient technologies that meet tomorrow's defense and aerospace challenges. To discuss partnership opportunities or learn more about our cutting-edge initiatives, please contact our research development team today.
- Temporal sequences for dynamic meltpond evolution tracking.
- Multi-spectral data integration to enhance classification richness.
- Neuromorphic hardware deployment to validate real-world energy savings.
- Field deployment on Arctic UAVs and edge compute systems for in-situ monitoring.
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About Parallax Advanced Research & The Ohio Aerospace Institute (OAI)
Parallax is a 501(c)(3) private nonprofit research institute that tackles global challenges through strategic partnerships with government, industry, and academia. It accelerates innovation, addresses critical global issues, and develops groundbreaking ideas with its partners. With offices in Ohio and Virginia, Parallax aims to deliver new solutions and speed them to market. In 2023, Parallax and OAI formed a collaborative affiliation to drive innovation and technological advancements in Ohio and for the nation. OAI plays a pivotal role in advancing the aerospace industry in Ohio and the nation by fostering collaborations between universities, aerospace industries, and government organizations, and managing aerospace research, education, and workforce development projects.
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Vision Lab : University of Dayton, Ohio
The Vision Lab is a Center of Excellence that develops new algorithms and architectures for real-time applications in the areas of signal processing, image processing, computer vision, pattern recognition, artificial neural networks and bio-mimetic object-vision recognition.udayton.edu
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Article titled āA Coming Revolution: Neuromorphic Computing is Key to AI Implementationā in the latest issue of Emerging Defense, featuring the neuromorphic research conducted by Steve Harbour and Parallax Advanced Research:
Steve Harbour shared a link to this article via LinkedIn and added:
āNeuromorphic AI is no longer science fictionāitās operational reality!
(ā¦) At Parallax Advanced Research, weāre building adaptive, explainable, accurate, and ultra-low-power neuromorphic systems for the most contested and obstructed areas & missions:
o Resilient edge AI
o Real-time RF signal classification
o Self-correcting sensor intelligence
o Human-interpretable autonomy
o Next: Data Centers, Server Farms, Cloud Computing, and GenAI
Weāre not mimicking the braināweāre engineering new, biologically-inspired intelligence and microsystems for NGA*, DARPA, Space, and beyond.ā
*NGA = āThe National Geospatial-Intelligence Agency is a combat support agency within the United States Depart Of Defense whose primary mission is collecting, analyzing, and distributing geospatial intelligence (GEOINT) to support national security.ā (https://en.m.wikipedia.org/wiki/National_Geospatial-Intelligence_Agency)
āA Coming Revolutionā | Dr. Steve Harbour, PhD
āA Coming Revolutionā ā Neuromorphic computing is key to AI implementation ā Neuromorphic AI is no longer science fictionāitās operational reality! Honored to have my work featured in the Spring 2025 issue of Emergent Defense (p.āÆ30): āA Coming Revolutionā ā Neuromorphic computing is key to AI...
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BrainShit
Regular
No, BrainChip is not involved.
Qualcomm uses the 3rd generation of the Qualcomm Hexagon NPU, which employs a DSP architecture specialized for parallel signal processing and AI.
BrainShit
Regular
Iāve always said itās Brainchip that tryās to implement the NDA with customer when they can, just canāt work out why some and not the others
Perhaps there will be a big revelation at some point, and several mega-corporations will look at each other because they now know that they all use the same secrect sauce .... Akida
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