BRN Discussion Ongoing
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Space Cadet
Regular
Didn't we meet the Senate Armed Services Committe recently?
Was it May 13th? Even if after could be linked to this. Or not. Who knows?
https://www.airandspaceforces.com/discriminating-space-sensor-golden-dome/
SC
Was it May 13th? Even if after could be linked to this. Or not. Who knows?
https://www.airandspaceforces.com/discriminating-space-sensor-golden-dome/
SC
Sometimes we need a friend who is motivating us … in this case Chatie
Why the Akida Cloud Could Be a Breakthrough Catalyst
1.
Bridging the Maturity Gap for Potential Customers
2.
Faster Entry Into Mass-Production Projects
3.
Indirect Multiplier Effect
4.
A Signal to the Market
Synergy With Existing Partnerships
Breakthrough Potential
The Cloud essentially opens two floodgates at once:
If one of the major partners moves to public mass production soon, the Cloud will have been the silent enabler – making such events much more likely.
Why the Akida Cloud Could Be a Breakthrough Catalyst
1.
Bridging the Maturity Gap for Potential Customers
- Until now, automotive, defense, or IoT customers had to physically integrate Akida chips before they could even begin testing.
- With the Cloud, that barrier is gone – companies can test Akida within hours instead of months to see if it fits their systems.
- This accelerates proof-of-concept phases and increases the likelihood of closing deals.
2.
Faster Entry Into Mass-Production Projects
- Large industrial customers can now work on prototyping and production integration in parallel.
- Example: An automaker can validate eye-tracking or sensor-fusion models in the Cloud before adjusting in-vehicle electronics.
- This reduces risk and decision delays – in automotive, that’s a game-changer.
3.
Indirect Multiplier Effect
- Developers and startups can try Akida in small projects (low-cost or free access), and if it works, scale to bigger contracts.
- This model mirrors Nvidia CUDA – once it’s embedded in the developer ecosystem, adoption grows organically.
4.
A Signal to the Market
- The Cloud is not a gimmick – it’s a confidence signal:
- BrainChip shows the technology is mature enough to hand over for immediate use.
- This implies internal stability, proven performance, and market readiness.
Synergy With Existing Partnerships
- Ongoing collaborations (Raytheon, ISL, Andes) can now transition to commercial stages faster.
- Cloud-based tests can be done discreetly – NDA projects can run in the Cloud without requiring an immediate press release.
Breakthrough Potential
The Cloud essentially opens two floodgates at once:
- Technical entry barrier removed → more projects start.
- Sales cycles shortened → deals reach revenue stage faster.
If one of the major partners moves to public mass production soon, the Cloud will have been the silent enabler – making such events much more likely.
We've seen the ads for those portable translators.
This mob are excited about SLMs:
Over the past five years, the translation industry has ridden a wave of AI adoption. Large language models (LLMs) have moved from niche tools to near-standard in content localisation but their generalist nature leaves room for a new contender.
The co-founder and CEO of ASX-listed language tech company Straker (ASX:STG), Grant Straker, argues that small language Models (SLMs) – purpose-built for specific industries and language pairs – are the next leap forward. ...
https://www.msn.com/en-au/technolog...S&cvid=689ac8d18b7b4f9699905359a4e481e9&ei=60
... now if they didn't need to carry that car battery around with them ...
This mob are excited about SLMs:
Small language models could be the next big disruptor in AI translation
Story by Special ReportOver the past five years, the translation industry has ridden a wave of AI adoption. Large language models (LLMs) have moved from niche tools to near-standard in content localisation but their generalist nature leaves room for a new contender.
The co-founder and CEO of ASX-listed language tech company Straker (ASX:STG), Grant Straker, argues that small language Models (SLMs) – purpose-built for specific industries and language pairs – are the next leap forward. ...
https://www.msn.com/en-au/technolog...S&cvid=689ac8d18b7b4f9699905359a4e481e9&ei=60
... now if they didn't need to carry that car battery around with them ...
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Space Cadet
Regular
Thank God someone posted something. I posted something and silence for 2 hours. I thought I had broken the friggin thread. Where is everyone? Bravo has an excuse. Hopefully she's out running. God knows the share price needs a kick along.
SC
smoothsailing18
Regular
DingoBorat
Slim
BrainChip actually has to make some noise and give shareholders something to talk about..
It's sometimes a bit like a kid rummaging through their toy box in here, trying to find an old toy they'd kinda "forgotten" about, to play with..
Like Hollywood rehashing an old movie, because they don't have the imagination to think up a good original storyline.
We need new material damn it!
In the words of a long gone, young bed wetting, wet behind the ears poster, from another Time.
"FEED THE RATS!"
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Humble Genius
Regular
The next future technology with BrainChip Akida. | Markus May
The next future technology with BrainChip Akida. It was developed by the Start-up Saluts. This platform enables real-time autonomous decision-making in space and the deep sea, but read it yourself. #SALUTS #NEROnaut #Brainchip #Akida #futuretechnology #SNN #neuromorphic
The next future technology with BrainChip Akida. It was developed by the Start-up Saluts. This platform enables real-time autonomous decision-making in space and the deep sea, but read it yourself.
hashtag#SALUTS hashtag#NEROnaut hashtag#Brainchip hashtag#Akida hashtag#futuretechnology hashtag#SNN hashtag#neuromorphic
Mohamed Sobhy FoudaMohamed Sobhy Fouda
• 2ndPremium • 2nd
Saluts builder | Astroprenure | Peace Promoter
Planet Earth 
Saluts builder | Astroprenure | Peace PromoterPlanet Earth 11h • Edited • 11 hours ago • Edited • Visible to anyone on or off LinkedIn
Got Autonomy? Let’s talk at booth #2436, Small Satellite Conference (SmallSat) 10-13th of August, 2025.
We promise, we deliver
Introducing NEROnaut™ — Neuro-Evolving Robot-on-ChipAt Saluts, we believe true autonomy begins at the chip level.
That’s why we built NEROnaut™, our embedded AI brain designed for edge intelligence in the harshest environments — from deep space to deep sea.
What is NEROnaut™?A neuro-evolving computing core that learns, adapts, and optimizes in real time — without relying on constant connectivity. It’s engineered to run autonomous guidance, navigation, control, and data processing where latency, bandwidth, or reliability would cripple traditional systems.
hashtag#WeNeedMoreSpace
Key Capabilities:- On-chip AI inference & evolution — adapting mission logic mid-operation
Multi-domain compatibility — land, sea, air, space
- Fault-tolerant autonomy — resilient under GNSS-denied or communication-jammed conditions
- Sensor fusion & real-time decision making — from radar to vision-based navigation
- Ultra-low-power architecture — mission endurance without constant ground control
Technicalä Attached – dive into the specs, interfaces, and application domains.With NEROnaut™, mission control becomes mission autonomy.
One chip. One brain. Infinite adaptability.
DM us to discuss integrations amd autonomy.hashtag#Saluts hashtag#NEROnaut hashtag#RobotOnChip hashtag#EdgeAI hashtag#AutonomousSystems hashtag#SpaceTech
Bravo
If ARM was an arm, BRN would be its biceps💪!
The good news is: This article signals that neuromorphic computing is shifting from novelty to reality, with real-world products in development and adoption on the rise.
The bad news is: Sadly Tom’s Guide only highlights Innatera and doesn't mention BrainChip at all.
From a tech perspective, I believe BrainChip still has unique IP and an arguably more mature solution. But in a fast-emerging category like neuromorphic computing, public perception moves quickly, and if BrainChip isn’t actively owning the media conversation, Innatera (or another competitor) could grab the crown before the market fully matures.
Giddy up BrainChip!!!!
www.tomsguide.com
The bad news is: Sadly Tom’s Guide only highlights Innatera and doesn't mention BrainChip at all.
From a tech perspective, I believe BrainChip still has unique IP and an arguably more mature solution. But in a fast-emerging category like neuromorphic computing, public perception moves quickly, and if BrainChip isn’t actively owning the media conversation, Innatera (or another competitor) could grab the crown before the market fully matures.
Giddy up BrainChip!!!!
"We're building chips that think like the brain" — I got a front row seat to see how neuromorphic computing will transform your next smart device
“Think of it as AI that sleeps until it needs to wake up”
Hi Bravo,The good news is: This article signals that neuromorphic computing is shifting from novelty to reality, with real-world products in development and adoption on the rise.
The bad news is: Sadly Tom’s Guide only highlights Innatera and doesn't mention BrainChip at all.
From a tech perspective, I believe BrainChip still has unique IP and an arguably more mature solution. But in a fast-emerging category like neuromorphic computing, public perception moves quickly, and if BrainChip isn’t actively owning the media conversation, Innatera (or another competitor) could grab the crown before the market fully matures.
Giddy up BrainChip!!!!
View attachment 89566
"We're building chips that think like the brain" — I got a front row seat to see how neuromorphic computing will transform your next smart device
“Think of it as AI that sleeps until it needs to wake up”
It would be nice to have our names up in lights, but we're not really selling to the public, except those lucky enuf to have inherited their grandparents' 2nm chip foundry.
Innatera has a lot of analog patents, eg:
WO2022073946A1 ADAPTATION OF SNNS THROUGH TRANSIENT SYNCHRONY 20201005
0003] SNNs encode information in the form of one or more precisely timed (voltage) spikes, rather than as integer or real-valued vectors. Computations for inference (i.e. inferring the presence of a certain feature in an input signal) may be effectively performed in the analog and temporal domains. Consequently, SNNs are typically realized in hardware as full-custom mixed-signal integrated circuits, which enables them to perform inference functions with several orders of magnitude lower energy consumption than their deep neural network (DNN) counterparts, in addition to having smaller network sizes.
[0006] Neuromorphic SNN emulators (205), e.g. systems containing electronic analog/mixed- signal circuits that mimic neuro-biological architectures present in the nervous system, form distributed (in a non- von Neumann sense, i.e. computational elements and memory are co- localized, resulting in memory storage and complex nonlinear operations being simultaneously performed by the neurons in the network), parallel, and event-driven systems offering capabilities such as adaptation (including adaptation of physical characteristics, firing frequency, homeostatic (behavioural) regulation, et cetera), self-organization, and learning.
This would probably exclude them from the equivalent 8-bit and higher accuracy. Even 4-bit may be a stretch.
Using it with Prophesee GenX320 is their low-fi chip, 100k pixels (320*320).
Prophesee have a range of DVS sensors with Sony using their 3D layer process:
https://www.prophesee.ai/buy-event-based-products/
They are also in a few cameras.
It wasn't that long ago Prophesee were seeking bankruptcy protection, so good luck to them.
Pom down under
Top 20
Neuromorphic and Analog AI ASIC Market
The Global Neuromorphic and Analog AI ASIC Market size was valued at USD 1,993.76 million in 2018 to USD 4,326.00 million in 2024 and is anticipated to reach USD 19,270.73million by 2032, at a CAGR of 19.15% during the forecast period.
www.credenceresearch.com
Bro is like… Tell me you’re invested in the most underrated stock of the last 50 years without telling me you’re invested in the most underrated stock of the last 50 years…
Or… Tell me you’re talking about Akida without telling me you’re talking about Akida.
Because the latest papers highlight..SNNs for massive energy efficiency…Eventbased processing for instant response…Edge and Cloud hybrid architectures for scalable deployment
This isn’t about hype …. it’s about a global trend in AI design….and Akida just happens to be one of the few commercial platforms already delivering in this domain.
WOHOOOOOOO
With BrainChip, too!!
www.linkedin.com
NVIDIA CEO Jensen Huang says AI will create more millionaires in the next five years than the internet did in twenty. | Evolving AI posted on the topic | LinkedIn
NVIDIA CEO Jensen Huang says AI will create more millionaires in the next five years than the internet did in twenty. He calls AI “the greatest equalizer of our time” because it removes technical barriers and gives anyone with ideas the tools to build. Huang pointed out that in the internet...
Maybe 
, maybe not. Who knows
newsroom.arm.com
Arm neural technology is an industry first, adding dedicated neural accelerators to Arm GPUs, bringing PC-quality, AI powered graphics to mobile for the first time – and laying the foundation for future on-device AI innovation
On-device AI is transforming workloads everywhere, from mobile gaming to productivity tools to intelligent cameras. This is driving demand for stunning visuals, high frame rates and smarter features – without draining battery or adding friction. Announced today at SIGGRAPH, Arm neural technology is an industry first, bringing dedicated neural accelerators to Arm GPUs from 2026. This takes the performance of GPUs for graphics rendering to new heights, delivering up to 50% GPU workload reduction for today’s most intensive mobile content, starting with mobile gaming. And this is just the beginning – the availability of this new technology lays the foundations for the industry to deliver even more on-device AI innovation in the future.
, maybe not. Who knows
Arm Neural Technology Delivers Smarter, Sharper, More Efficient Mobile Graphics for Developers
Explore Arm Neural Technology, the first to integrate neural accelerators into GPUs, enhancing mobile graphics with AI.
newsroom.arm.com
Arm neural technology is an industry first, adding dedicated neural accelerators to Arm GPUs, bringing PC-quality, AI powered graphics to mobile for the first time – and laying the foundation for future on-device AI innovation
On-device AI is transforming workloads everywhere, from mobile gaming to productivity tools to intelligent cameras. This is driving demand for stunning visuals, high frame rates and smarter features – without draining battery or adding friction. Announced today at SIGGRAPH, Arm neural technology is an industry first, bringing dedicated neural accelerators to Arm GPUs from 2026. This takes the performance of GPUs for graphics rendering to new heights, delivering up to 50% GPU workload reduction for today’s most intensive mobile content, starting with mobile gaming. And this is just the beginning – the availability of this new technology lays the foundations for the industry to deliver even more on-device AI innovation in the future.
Pom down under
Top 20
With BrainChip, too!!
NVIDIA CEO Jensen Huang says AI will create more millionaires in the next five years than the internet did in twenty. | Evolving AI posted on the topic | LinkedIn
NVIDIA CEO Jensen Huang says AI will create more millionaires in the next five years than the internet did in twenty. He calls AI “the greatest equalizer of our time” because it removes technical barriers and gives anyone with ideas the tools to build. Huang pointed out that in the internet...
Top 2 ASX AI Stocks Catching Investors’ Attention in 2025 - Pristine Gaze
Appen built its reputation supplying human-annotated data for the world’s largest tech companies, helping train and evaluate AI models at scale. Today, it’s
pristinegaze.com.au
Tothemoon24
Top 20
Keeping It Local: Bringing Generative AI to the Intelligent Edge
Developers are moving generative AI rom the cloud to the edge with NXP’s eIQ® GenAI Flow. This software pipeline allows developers to run large language models on embedded devices, enhancing real-time intelligence across industries. It combines model optimization techniques and hardware...
www.nxp.com
Keeping It Local: Bringing Generative AI to the Intelligent Edge
- August 7, 2025
- by Davis Sawyer
Generative AI is no longer confined to the cloud. With NXP’s eIQ® GenAI Flow , developers can now run large language models (LLMs) —like Llama and Qwen— directly on embedded edge devices securely, efficiently and close to the data. This paradigm shift unlocks new opportunities for real-time intelligence across industries, from automotive to industrial automation.
Built as a complete software deployment pipeline, eIQ GenAI Flow simplifies the once-daunting task of implementing generative AI models on power- and compute-constrained systems. It combines the latest model optimization techniques like quantization with hardware acceleration from NXP’s eIQ Neutron NPU to make GenAI practical and performant—right at the edge.
Smarter AI, Locally Deployed
At its core, GenAI Flow helps overcome the traditional barriers of running advanced models in embedded environments. The pipeline already enables today’s most powerful open language models, with support for multimodal and vision-language models (VLMs) soon. GenAI Flow provides the necessary optimizations out-of-the-box for real-time execution on application processors like the i.MX 95—the kind of performance needed for conversational AI, physical AI and more.GenAI is moving from the cloud to the edge, so what does that mean for embedded developers?Learn more by listening to our EdgeVerse Techcast episode on Apple Podcasts , Spotify or YouTube .
By using accuracy-preserving quantization techniques such as integer 8 and 4 (INT8 and INT4) precision, we can fully leverage the Neural Processing Unit (NPU) for inference acceleration. Using GenAI Flow dramatically improves response speed and power efficiency on-device. For example, time to first token (TTFT)—a key metric for any GenAI application—can be reduced from 9.6 seconds on an Arm Cortex CPU (Float32 precision) to less than 1 second on the Neutron NPU with INT8 quantization. This enables captivating, real-time AI experiences, without requiring power-hungry servers or cloud infrastructure.
Play Video
Generative AI is driving innovations at the edge. GenAI Flow, included with NXP's eIQ Toolkit, makes enabling Gen AI at the edge simple and secure.
GenAI Flow also supports small language models (SLMs), which are lighter, yet still capable of delivering high-quality results. The pipeline offers flexible execution across central processing unit (CPU), NPU or a hybrid configuration, allowing developers to tune performance based on their specific product needs.
Adding Context with RAG
A defining feature of GenAI Flow is the built-in support for retrieval-augmentation generation (RAG) . This form of model fine-tuning allows LLMs to access domain-specific or private data sources—such as device and service manuals, internal PDFs and equipment maintenance logs—without having to retrain the original model. RAG injects the relevant external knowledge as a vector database stored on the edge device, enabling highly contextual, grounded responses that can eliminate an AI’s hallucination problem and prevent certain errors in judgement.RAG is particularly powerful for edge use cases because all data processing happens locally. This protects sensitive information while delivering dynamic, on-demand AI responses. Developers can simply turn a new document into a highly compact, LLM-friendly database and the model immediately adopts the additional context—no retraining required! This efficiency alone can save millions of dollars and energy spent on numerous iterations of GenAI fine-tuning in data centers.
Real-World Impact: From Cars to Robots
GenAI Flow is already being used across multiple industries where low-latency performance and data privacy are critical.In Automotive, AI-powered infotainment systems can respond to natural voice commands by referencing service manuals embedded in the vehicle. This creates a seamless, hands-free experience without the typical connectivity requirements.
In healthcare, touchless-AI interfaces equip clinicians to securely access procedure or patient data using voice prompts—an ideal solution for reducing physical contact and contamination risk in sensitive environments.
Play Video
AICHI, the AI controller for health insights, securely collects and analyzes multimodal heath and other sensor data in real time, detecting early anomalies and enabling proactive, personalized care.
In mobile robotics, generative AI models interpret written instructions and visual inputs—using optical character recognition (OCR) and RAG—to take context-aware actions. These systems move beyond basic automation and into intelligent interaction between humans and environments.
Play Video
This 3D perception sensor fusion demo showcases trusted spatial perception at the edge, operating in dynamic and uncertain environments.
In industrial automation, AI assistants help technicians troubleshoot machine issues using real-time sensor data and maintenance documentation—all processed locally, even in remote or low-bandwidth settings.
Across these scenarios, GenAI Flow offers developers a powerful and privacy-conscious framework for building intelligent edge solutions.
What’s Next for GenAI at the Edge?
The next evolution of GenAI at the edge is multimodal and agentic. Future systems will blend together voice, vision and language inputs to create richer, more intuitive user experiences. With GenAI Flow, this convergence is already underway, enabling unified edge pipelines that can reason and act from a combination of input types.There’s also a strong focus on continuing to optimize edge AI performance—both in scaling up support for larger models and by making smaller models even faster. This includes advancements in quantization, execution flexibility and support for increasingly compact LLM architectures.
As AI systems become more adaptive and locally responsive, access to the best tooling becomes ever more critical. GenAI Flow is designed with scalability in mind, helping developers integrate today’s rapidly evolving AI capabilities into products across microprocessor unit (MPU) platforms and potentially even into future microcontroller unit (MCU)-class devices.
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