BRN Discussion Ongoing
- Thread starter TechGirl
- Start date
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!"
Last edited:
Humble Genius
Regular
Got Autonomy?! NEROnaut: Neuro-Evolving Robot On-Chip | 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: AI will create more millionaires than the internet did | 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: AI will create more millionaires than the internet did | 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.
Tags: Technologies
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.
Tags: Technologies
NXP is showing how Edge GenAI is becoming reality right now. Akida operates in exactly the same segment – and with the GenAI FPGA Development Platform, it’s already in the game technologically. The difference? No flashy marketing, just solid substance.
When the market wakes up to this, I can only see one thing happening: panic buying.
Baron Von Ricta
Regular
Hey Boeing,NXP is showing how Edge GenAI is becoming reality right now. Akida operates in exactly the same segment – and with the GenAI FPGA Development Platform, it’s already in the game technologically. The difference? No flashy marketing, just solid substance.
When the market wakes up to this, I can only see one thing happening: panic buying.
I've been panic buying for 8 years. I'm just starting to relax
I’ve also come across this from NXP.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.
Tags: Technologies
Smarter Wireless Sensing at the Edge with MCX W23 MCU | NXP Semiconductors
The next leap in intelligent sensing is wireless, scalable, and built for the edge 📡📈🌐 With the launch of the MCX W23, we’re empowering developers to build smarter, smaller, and longer-lasting wireless sensing devices — from medical wearables to industrial actuators. The MCX W23 is our new...
The next leap in intelligent sensing is wireless, scalable, and built for the edge
With the launch of the MCX W23, we’re empowering developers to build smarter, smaller, and longer-lasting wireless sensing devices — from medical wearables to industrial actuators.
The MCX W23 is our new dedicated wireless MCU platform, designed for battery-powered edge applications where size, efficiency, and reliability matter most.
Bravo
If ARM was an arm, BRN would be its biceps💪!
View attachment 89543
Lockheed explicitly says future interceptors will require “space-based sensors and onboard processing” for in-orbit targeting decisions.
This is the kind of radiation-tolerant edge AI where BrainChip has a precedent. Thinking here about Frontgrade Gaisler radiation-hardened systems for space, already licensed Akida IP.
View attachment 89544
EXTRACT 1
View attachment 89548
EXTRACT 2
View attachment 89547
Layered Defense, Orbital Advantage
Space is no longer a supporting player. It’s the heart of America’s multi-layered missile defense strategy and the cornerstone of America’s future defense.www.lockheedmartin.com
Lockheed Martin gets another mention here in relation to the Golden Dome, as does RTX.
The other thing I noticed is that the article says "Notably, the slides did not mention Mr Elon Musk’s SpaceX, which was part of a bid for Golden Dome contracts alongside software maker Palantir and defence systems manufacturer Anduril."
Is it merely a coincidence that Jonathan Tapson also mentioned both Palantir and Anduril in his Washington post?
Pentagon Golden Dome to have 4-layer defence system, slides show
The Golden Dome missile defence system faces an ambitious 2028 deadline set by US President Donald Trump himself.
PHOTO: REUTERS
Follow topic:
PentagonPublished Aug 13, 2025, 06:33 AM
Updated Aug 13, 2025, 06:53 AM
WASHINGTON - The Trump administration's flagship Golden Dome missile defence system will include four layers - one satellite-based and three on land - with 11 short-range batteries located across the continental US, Alaska and Hawaii, according to a US government slide presentation on the project first reported by Reuters.
The slides, tagged “Go Fast, Think Big!” were presented to 3,000 defence contractors in Huntsville, Alabama, last week and reveal the unprecedented complexity of the system, which faces an ambitious 2028 deadline set by US President Donald Trump.
The system is estimated to cost US$175 billion (S$224.53 billion), but the slides show uncertainties still loom over the basic architecture of the project because the number of launchers, interceptors, ground stations, and missile sites needed for the system has yet to be determined.
"They have a lot of money, but they don't have a target of what it costs yet," said one US official.
So far, Congress has appropriated US$25 billion for Golden Dome in Mr Trump’s tax-and-spend Bill passed in July.
Another US$45.3 billion is earmarked for Golden Dome in his 2026 presidential budget request.
Intended as a multi-layered missile defence shield for the United States, Golden Dome draws inspiration from Israel's Iron Dome, but is significantly bigger due to the geography it will need to protect and the complexity due to the varied threats it will face.
According to the slides, the system architecture consists of four integrated layers: a space-based sensing and targeting layer for missile warning and tracking as well as "missile defence" and three land-based layers consisting of missile interceptors, radar arrays, and potentially lasers.
One surprise was a new large missile field - seemingly in the Midwest according to a map contained in the presentation - for Next Generation Interceptors (NGI) which are made by Lockheed Martin and would be a part of the "upper layer" alongside Terminal High Altitude Area Defense (Thaad) Aegis systems which are also made by Lockheed.
NGI is the modernised missile for the Ground-Based Midcourse Defence (GMD) network of radars, interceptors and other equipment - currently the primary missile defence shield to protect the United States from intercontinental ballistic missiles from rogue states.
The US operates GMD launch sites in southern California and Alaska. This plan would add a third site in the Midwest to counter additional threats.
Other technical hurdles the slides identified included communication latency across the "kill chain" of systems.
Contractors such as Lockheed, Northrop Grumman, RTX, and Boeing have a variety of missile defence systems.
Notably, the slides did not mention Mr Elon Musk’s SpaceX, which was part of a bid for Golden Dome contracts alongside software maker Palantir and defence systems manufacturer Anduril.
The Pentagon said it is gathering information "from industry, academia, national labs, and other government agencies for support to Golden Dome" but it would be "imprudent" to release more information on a programme in these early stages.
One key goal for Golden Dome is to shoot targets down during their “boost phase,” the slow and predictable climb through the Earth's atmosphere of a missile.
Rather, it seeks to field space-based interceptors that can more quickly intercept incoming missiles.
The presentation highlighted that the United States "has built both interceptors and re-entry vehicles" but has never built a vehicle that can handle the heat of reentry while targeting an enemy missile.
The last lines of defence dubbed the "under layer" and "Limited Area Defence" will include new radars and current systems like the Patriot missile defence system and a new "common" launcher that will launch current and future interceptors against all threat types.
These modular and relocatable systems would be designed to minimise reliance on prepared sites, allowing for rapid deployment across multiple theatres.
Space Force General Michael Guetlein, confirmed in July to lead the Golden Dome project, has 30 days from his July 17 confirmation to build a team, another 60 days to deliver an initial system design, and 120 days to present a full implementation plan, including satellite and ground station details, people briefed on a memo signed by Defence Secretary Pete Hegseth have told Reuters. REUTERS
Pentagon Golden Dome to have 4-layer defence system, slides show
The system is estimated to cost US$175 billion (S$224.53 billion). Read more at straitstimes.com. Read more at straitstimes.com.
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