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GlobalFoundries buys MIPS for edge AI network dominion​

The chip maker banks on the promise of RISC-V
July 09, 2025 By Giacomo "Jack" Lee Comment
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GlobalFoundries announced a definitive agreement to acquire chip design company MIPS, in a move focused around AI-enabled network infrastructure.
In its announcement from Tuesday, the New York-headquartered foundry firm shared that MIPS will continue to operate as a standalone business within the new setup. That business revolves around developing processor architectures, now complementing GlobalFoundries’ (GF) semiconductor manufacturing capabilities.
What makes MIPS particularly attractive to GF is its relevance in AI chip design. Recent years have seen the Sunnyvale, California business focus on RISC-V, poaching execs from the likes of AMD and RISC-V rival SiFive to bolster its mission.
Pronounced risk-five, RISC-V is an open standard instruction set (ISA) architecture based on established reduced instruction set computing (RISC) principles. Based on this, the firm’s Atlas portfolio offers compute cores designed for real-time, low-latency processing and, perhaps most pertinently, edge AI processing cores.
With the acquisition, GF is in a strong position to offer custom chip solutions for smart routers, switches, and edge-network devices that incorporate AI and machine learning functions closer to the network’s edge.
With in-house processor IP and fabrication, its custom chips are likely to attract the likes of network equipment vendors such as Cisco, and OEMs like Nokia for SoCs in 5G small cells and base stations.

AI on the edge​

The low-latency and high bandwidth efficiency of AI directly embedded in edge devices is driving more interest in the market.
As reported by SDxCentral, most initial investment in edge AI is targeted at equipment that enterprises use to support on-prem use cases.
Companies like Verizon have already signed deals with the likes of Nvidia to power enterprise AI services. While ROI remains low and IoT persists as the main edge use case driver, IDC predicts the edge compute market is set for an AI-fueled boost pushing overall segment spend to $380 billion by 2028.
With MIPS on board, GF finds itself in a good position for all this loot. That said, how big of a deal the acquisition is has not been disclosed. The last public record of a MIPS sale was for $60 million, when Tallwood Venture Capital bought the company from Imagination Technologies in 2017.
Since then, the company has changed hands a few times, being bought out by Wave Computing in 2018 for an undisclosed amount.
According to Reuters, Shanghai’s CIP United acquired full licensing rights to MIPS architecture for mainland China and its territories in 2019 for $60 million. Those rights for China will remain in CIP's hands in spite of GF’s global operations of MIPS.
GF’s acquisition of MIPS is expected to close in the second half of 2025.

"IDC predicts the edge compute market is set for an AI-fueled boost pushing overall segment spend to $380 billion by 2028."

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Fixer Wallet Retrieval possesses a wealth of knowledge in the cryptocurrency space, particularly in identifying scams and recovering lost funds. They walked me through the entire recovery process, outlining the steps we needed to take. Their transparency and commitment to helping me regain my lost investment were evident throughout our interactions. It all started innocently enough. I was excited about the potential of cryptocurrency and eager to invest in a promising new project. Little did I know, the website I stumbled upon was a facade designed to lure unsuspecting investors like me. After a few clicks, my funds were gone, and the realization hit hard: I had been scammed. As I dug deeper into the world of crypto recovery, I stumbled upon Fixer Wallet Retrieval. A name that sparked curiosity and hope, I began researching this enigmatic figure. Fixer wallet Retrieval was known for his unique approach to helping victims reclaim their stolen cryptocurrencies. With a track record of successful recoveries and countless testimonials, I knew I had to reach out Contact WhatsApp: ‪‪‪‪‪‪‪‪‪‪+447599188182‬‬‬‬‬‬‬‬‬‬

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Esq.111

Fascinatingly Intuitive.
Fixer Wallet Retrieval possesses a wealth of knowledge in the cryptocurrency space, particularly in identifying scams and recovering lost funds. They walked me through the entire recovery process, outlining the steps we needed to take. Their transparency and commitment to helping me regain my lost investment were evident throughout our interactions. It all started innocently enough. I was excited about the potential of cryptocurrency and eager to invest in a promising new project. Little did I know, the website I stumbled upon was a facade designed to lure unsuspecting investors like me. After a few clicks, my funds were gone, and the realization hit hard: I had been scammed. As I dug deeper into the world of crypto recovery, I stumbled upon Fixer Wallet Retrieval. A name that sparked curiosity and hope, I began researching this enigmatic figure. Fixer wallet Retrieval was known for his unique approach to helping victims reclaim their stolen cryptocurrencies. With a track record of successful recoveries and countless testimonials, I knew I had to reach out Contact WhatsApp: ‪‪‪‪‪‪‪‪‪‪+447599188182‬‬‬‬‬‬‬‬‬‬

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Email fixerwalletretrieval@fixer.co.site
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Felix

Emerged
Fixer Wallet Retrieval possesses a wealth of knowledge in the cryptocurrency space, particularly in identifying scams and recovering lost funds. They walked me through the entire recovery process, outlining the steps we needed to take. Their transparency and commitment to helping me regain my lost investment were evident throughout our interactions. It all started innocently enough. I was excited about the potential of cryptocurrency and eager to invest in a promising new project. Little did I know, the website I stumbled upon was a facade designed to lure unsuspecting investors like me. After a few clicks, my funds were gone, and the realization hit hard: I had been scammed. As I dug deeper into the world of crypto recovery, I stumbled upon Fixer Wallet Retrieval. A name that sparked curiosity and hope, I began researching this enigmatic figure. Fixer wallet Retrieval was known for his unique approach to helping victims reclaim their stolen cryptocurrencies. With a track record of successful recoveries and countless testimonials, I knew I had to reach out Contact WhatsApp: ‪‪‪‪‪‪‪‪‪‪+447599188182‬‬‬‬‬‬‬‬‬‬

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Email fixerwalletretrieval@fixer.co.site

Oh really

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RobjHunt

Regular
Bigger business with work from home staff, staff on the road or travelling and sub office staff all carry devices including lap tops, phones, printers, etc that access the mainframe network. These devices provide openings for hackers.
Government departments have a lot of work from home staff which increase the vulnerability for cyberattack. We have seen plenty of cyberattacks in Australia where private data has been stolen.
Not sure what the C and a couple of Y's is:rolleyes:
I was referring Dodgyknees references to Cybersecurity.
 
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Tothemoon24

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The Missing Piece of Smart Things Manufacturing 🧠⚡

Remember our vision of 3D printing smart shipping boxes and supply chain sensors at any corner store earlier this week? One underlying technology that can make it possible are new chips that think like the human brain.

Neuromorphic edge chips are now so small and efficient they can be embedded anywhere. We're talking tiny 1-milliwatt processors that work like our brains do—100x faster processing and 500x lower energy consumption. 🚀

Companies like BrainChip and SynSense are developing these today. While not ready for any old box yet, they're rapidly approaching the point where intelligence becomes as standard as plastic in manufacturing.

What becomes possible when 1-milliwatt intelligence gets embedded anywhere? 💡
📦 Smart packaging on pallets that knows when something's wrong
🔐 Product-level monitoring with chips smart enough to detect issues
📊 Equipment sensors that understand their environment and alert you instantly
⚡ Connected intelligence in boxes, products, even intelligent documents

Here's the breakthrough: 🎯
These chips literally work like our brains do—they only activate when something happens. Smart enough to understand when something's wrong, connected enough to let you know instantly.

🧠 Brain-inspired processing that mimics human neurons
🔋 1-milliwatt power - operates for months on minimal energy
💾 Microscopic size - getting small enough for embedding anywhere
💰 Incredible economics - intelligence approaching the cost of a sticker

Imagine designing things by specifying not just shape and material, but exactly where to place micro-intelligence during printing. Every object emerges already smart, already connected.

What becomes possible when intelligence is built into the manufacturing process? 🤔 The answer is reshaping entire industries—and we're just getting started. 🌍
#Innovation #3DPrinting #SmartObjects #SupplyChain #Logistics #Manufacturing #EdgeComputing #Neuromorphi


The Missing Piece of Smart Things Manufacturing 🧠⚡

Remember our vision of 3D printing smart shipping boxes and supply chain sensors at any corner store earlier this week? One underlying technology that can make it possible are new chips that think like the human brain.

Neuromorphic edge chips are now so small and efficient they can be embedded anywhere. We're talking tiny 1-milliwatt processors that work like our brains do—100x faster processing and 500x lower energy consumption. 🚀

Companies like BrainChip and SynSense are developing these today. While not ready for any old box yet, they're rapidly approaching the point where intelligence becomes as standard as plastic in manufacturing.

What becomes possible when 1-milliwatt intelligence gets embedded anywhere? 💡
📦 Smart packaging on pallets that knows when something's wrong
🔐 Product-level monitoring with chips smart enough to detect issues
📊 Equipment sensors that understand their environment and alert you instantly
⚡ Connected intelligence in boxes, products, even intelligent documents

Here's the breakthrough: 🎯
These chips literally work like our brains do—they only activate when something happens. Smart enough to understand when something's wrong, connected enough to let you know instantly.

🧠 Brain-inspired processing that mimics human neurons
🔋 1-milliwatt power - operates for months on minimal energy
💾 Microscopic size - getting small enough for embedding anywhere
💰 Incredible economics - intelligence approaching the cost of a sticker

Imagine designing things by specifying not just shape and material, but exactly where to place micro-intelligence during printing. Every object emerges already smart, already connected.

What becomes possible when intelligence is built into the manufacturing process? 🤔 The answer is reshaping entire industries—and we're just getting started. 🌍
#Innovation #3DPrinting #SmartObjects #SupplyChain #Logistics #Manufacturing #EdgeComputing #Neuromorphi



IMG_1238.jpeg
 
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A possible company to keep an eye on:


"Integer Technologies is an applied research and product development company founded by scientists and engineers with a passion for protecting freedom with innovation. We perform R&D on next-generation systems and technologies for the Department of Defense and other U.S. Government agencies. We are hardware and software developers with experience transforming research into fieldable technology. Our core portfolio of research includes projects in powerenergy systems, unmanned systems (with an emphasis on maritime systems), digital engineering, cyber security, and advanced manufacturing. Our mission is to create a safer world by translating scientific discoveries into reliable products that address urgent national security needs... at the speed of relevance."

One of the jobs desired qualifications:

"Background in AI/ML applications for edge deployment, including model optimization techniques (e.g., pruning, quantization, or neuromorphic computing)"

A link if you're interested to know a bit more about Integer Technologies:

 
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Frangipani

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Speaking of Silicon Valley: The position of “Sales Director US/Bay Area” is still open after more than five months of advertising it:
(Cf. https://thestockexchange.com.au/threads/brn-discussion-ongoing.1/post-445809).

I am somewhat surprised our company still hasn’t found anyone deemed suitable to fill this position (which will be reporting directly to the VP of Global Sales), given our KMP clearly saw a need by creating this new job.

Or is the problem possibly a lack of applicants due to the high requirements listed under “Essential job duties and responsibilities” and “Qualifications” that may come across as overwhelming to some?

Do qualified and interested candidates possibly shy away from applying when reading things like “Expectation to complete at least one contract/deal within your first year of employment” against the backdrop of our company’s woeful financials and minimalist IP sales track record so far, wondering whether they will risk being given the boot within a year of starting their new job or even worse fear the company may not survive financially?

Note that potential applicants with a business, marketing or engineering degree, although experienced in the semiconductor or technology industry, may not necessarily be familiar with neuromorphic technology and its future prospects, yet. At the end of the day, they might prefer to go with an established big player or a smaller, but already meaningful revenue-generating company.
After all, the Bay Area is teeming with companies in the semiconductor business, from giant global players to promising startups.


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One month later…

Another attempt at finding a suitable candidate for the advertised position as “Sales Director, US/ Bay Area” - and it doesn’t seem to be a “lack of applicants” matter in general…


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Frangipani

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Laurent Hili on the ESA-supported BrainChip -Frontgrade Gaisler partnership:


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Diogenese

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Frangipani

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Cloud based AI vs Edge AI Topology

Cloud based AI vs Edge AI Topology

Connected, Low-power, Intelligent Devices at the Edge​


HaiLa

4,385 followers

July 10, 2025
Article by Patricia Bower, HaiLa VP Product

What’s the ‘Edge’ in Edge AI?​

The amount of data generated by billions of wireless connected devices—such as sensors, wearables, and smart appliances—is growing at an incredible rate. Analysts forecast global growth from 20 billion devices today to over 40 billion by 2030 (IoT Analytics, State of IoT 2024).

Using today’s communications networks, user data is sent from (for example) the smartwatch on your wrist to datacenters located miles from you or even across a continent. The data is processed in these facilities by clusters of artificial intelligence (AI) or machine learning (ML) systems and is generally referred to as cloud compute. There are three issues with this model:

  1. The time it takes for the data to traverse communications networks to the datacenter (latency)
  2. The heavy load on datacenter AI clusters, and resulting power consumption, for processing raw data from billions of devices
  3. The higher potential for personal data privacy breaches

The growing need for faster responses (real-time decision making), reduced loading on datacenters (cloud offload), and improved privacy (anonymized data) has therefore driven the rise of edge AI—where intelligent processing happens directly on the device itself, such as in a sensor or wearable. At the core of this shift are neural processing units (NPUs)—specialized AI processors that enable devices to "think locally” and to rely on optimized AI and ML training models.

Chip technology for datacenter AI processing is extremely energy intense. In these AI clusters, where complex computational tasks - such as climate modelling - are performed, compute operations are measured in exaFLOPS, or a million, trillion floating-point operations per second. Edge AI NPUs can support up to a few teraOPS, or trillion operations per second OPS, and are purpose-built to be extremely power efficient. This is important as many connected devices run on batteries.

What Applications are Driving Edge AI?​

Edge AI is already transforming many industries. Here are some notable applications:

Patient Monitoring
Hospitals and home-care settings are increasingly using AI-powered sensors to continuously monitor vital signs such as heart rate, respiration, and oxygen levels. By analyzing these signals locally, devices can detect early warning signs of health deterioration and alert caregivers—without the need to stream personal health data to the cloud. This approach protects patient privacy and saves bandwidth.

Personal Health Monitoring
Wearable devices like fitness trackers and smartwatches are incorporating edge AI to offer more intelligent insights into sleep patterns, stress levels, and activity trends. These devices process sensor data in real time, providing instant feedback and recommendations on the go while preserving battery life.

Retail
In retail environments, smart cameras and sensors are used for foot traffic analysis, shelf inventory monitoring, and customer behavior tracking. Edge AI allows these insights to be processed locally without storing or transmitting images, which supports both privacy and efficiency. For example, a store can detect when a shelf is empty and trigger restocking alerts automatically. Using a battery powered, modular solution with integrated, low-power Wi-Fi provides a simple installation into legacy environments.

Industrial Asset Monitoring and Anomaly Detection
Factories and industrial sites rely on sensors to monitor machinery. Edge AI enables these sensors to learn the normal operating patterns of equipment and detect anomalies like unusual vibrations or temperature spikes—early signs of failure. This allows for predictive maintenance, reducing downtime and maintenance costs.

The ability to collect intelligent data directly from edge devices reduces the need for cloud infrastructure and data transmission, which means that edge AI also lowers the overall cost of system deployment and operation.

Leveraging Low-power RF and Neuromorphic AI for Efficient Edge AI​

A major technical challenge in deploying edge AI is power consumption. Devices like health monitors or industrial sensors are often required to run for months or years on small batteries—or in some cases, without batteries at all. As mentioned, NPUs optimized for low power are an essential element to perform AI tasks like image recognition or anomaly detection using minimal energy. To address this need, BrainChip’s Akida™ neuromorphic technology relies on the principle of sparsity for power efficient, event-driven AI compute.

The inferenced data from NPU AI processing is only part of the equation. Devices must also have the ability to connect and collate this intelligent data from multiple devices in a local or personal area network. This is where low-power radio frequency (RF) technologies like Wi-Fi and Bluetooth Low Energy come in and where HaiLa is setting a new paradigm in just how efficient data transmission can be over these protocols. HaiLa’s extreme low power radio communications technology, paired with power-optimized edge compute, allows devices to send and receive inferenced data without draining energy reserves, making them perfect partners for edge AI systems.

HaiLa and BrainChip: Sensors Converge 2025 Connected Edge AI Demo​

At Sensors Converge in Santa Clara this year, HaiLa and BrainChip joined forces to demonstrate object classification via extreme low-power Wi-Fi.

Article content

HaiLa and BrainChip collaborated to showcase very low power Connected Edge AI Object Classification

This hardware prototype includes a camera module to capture object images, BrainChip’s NPU test platform which is pre-trained to recognize objects from a camera image capture, and HaiLa’s BSC2000 extreme low-power Wi-Fi radio chip which transmits the image class and type data via Wi-Fi to display as a simple icon on a dashboard.

The demo illustrates the potential use of connected, edge AI for applications using image recognition and classification in industrial, retail, and medical sectors where cost effective, low latency, and private data connections are key requirements.

The Future of Edge AI: Connected, Low-power, Intelligent Devices Everywhere​

HaiLa’s core specialization in extreme low-power radio technology over standard protocols like Wi-Fi, Bluetooth, and even cellular, delivers one of the critical enablers of pervasive edge AI: extreme low-power data transmission. Together with BrainChip’s innovative edge compute, this opens up a broad range of options for end-users to support energy-efficient, on-device AI.

Edge AI represents a powerful shift in how intelligent systems operate—bringing the power of AI directly to the devices at the heart of the connected world. By combining efficient NPUs with low-power wireless communication, edge AI systems can run independently, securely, and with minimal energy use. As more industries adopt this technology, we can expect smarter, more responsive, and more sustainable solutions for multiple applications.
Contact us to learn more:

info@haila.io
sales@brainchip.com
 
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Frangipani

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Joao Martins, Editor-in-Chief of audioExpress, reports about his visit to Sensors Converge 2025 in The Audio Voice, “the Weekly Newsletter for the Audio Industry and Audio Product Developers”. Here are some excerpts and photos:


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(…)


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(…)

From his audio-centric perspective, Joao Martins says all in all he was rather disappointed in this year’s Sensors Converge:

“I might be missing something, but what's the meaning of attending a sensors show to promote "IoT”? It's an obsolete terminology for something that was touted 10 years ago. No one wants devices "connected to the Internet" in the age of "edge AI" if you really want to push a buzzword. This is the age of self-sustained intelligence of things. Where sensors make sense but are mere extensions.

How those common threads could possibly converge around real-world applications was for me the missing link at this show. I could find many demonstrations that had to do with audio, and I've seen multiple references to hearing, earbuds, and even voice, but nothing that wasn't already demonstrated at other shows, sometimes going back as much as four years.

Together with the absence of many key companies in this space, I couldn't help feeling disappointed that there were no groundbreaking presentations on audio applications. And yet, some of the companies who hold the technology to make those happen were there, but they simply just didn't mention the applications.”



One of two personal highlights for him was a presentation by Mouna Elkhatib, CEO/CTO of AONDevices (who worked for BrainChip from December 2015 to May 2016; she and Peter van der Made are co-inventors of several patents, cf. https://patents.justia.com/inventor/mouna-elkhatib).

AONDevices certainly sound like a serious contender to BrainChip in the hearables market:

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(…)
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(…)
 
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Frangipani

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Our partner fortiss and their partner Interactive Wear (a 20 year old company that emerged out of a management buy-out of Infineon Technologies’ Wearable Technology Solutions) will be “presenting neuromorphic computing and its applications in the defense sector” at the upcoming SVI Future Congress (SVI = Systemverbund Verteidigung im Innovationsraum) in Munich, aimed at a target audience [LOL 🎯] of security and defense industry experts.

I couldn’t find any additional information online about the collaboration between fortiss and Interactive Wear.



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The event website of the fully-booked conference links to a list of countries published by the German Federal Ministry of the Interior regarding security clearance concerns.
Citizens of countries listed are barred from attending the 8/9 July conference.

Somewhat surprisingly, Ukraine is also on that list, which was published on June 8, 2022, just over three months after Russia had invaded Ukraine (again), and hasn’t been updated since. Possibly due to concerns about pro-Putin ethnic Russians with Ukrainian citizenship?

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Today, Axel von Arnim shared more information and photos about what fortiss and Interactive Wear were exhibiting at TechHub SVI 2025 earlier this week 👆🏻:

Showcasing our neuromorphicly gesture-driven virtual mule robot for defence applications at the#TechHub-SVI defence conference in Munich. Together with our partners [sic] #InteractiveWear, we deliver wearable smart sensing solutions for defence and the medtech industry.”

While it doesn’t look like Akida was already part of that collaboration (not surprisingly, given the partnership between fortiss and BrainChip appears to be still quite young) and I can only spot Loihi, the showcase nevertheless demonstrates what fortiss mean when they say about themselves:

“As the Research Institute of the Free State of Bavaria for software-intensive Systems, fortiss stands for application-oriented cutting-edge research and sets standards in the research and transfer of highly innovative technologies. In close cooperation with academic and practice-oriented partners, we act as a bridge between science and industry and develop excellent solutions for the most pressing challenges of the digital world.”


On second thought: Have a look at the last two photos. Could that possibly be an Akida M.2 factor on the very left of the screen, which shows the Sensor Fusion Development Plattform MHX?
Doesn’t look identical, but similar? 🤔




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Frangipani

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No doubt Frontgrade Gaisler will be spruiking GR801 - their first neuromorphic AI solution for space, powered by Akida - during their upcoming Asia Tour through India, Singapore and Japan:


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Frangipani

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Frangipani

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Last Friday, TH Nürnberg (Nuremberg Institute of Technology) had an Open Day in connection with a barbie. Wow, they surely must have grilled lots of Nürnberger Rostbratwürstchen, the city’s signature finger-long sausages flavoured with marjoram that Johann Wolfgang von Goethe is said to have loved so much that he had some sent to Weimar by (horse-powered) express mail…

(webpage in German only: https://www.th-nuernberg.de/studium-karriere/studienorientierung-und-studienwahl/studienwahl-bbq/)

Christian Axenie, Head of TH Nürnberg’s SPICES Lab, whose team came runner-up in the 2023 tinyML Pedestrian Hackathon utilising Akida (cf. https://iopscience.iop.org/article/10.1088/2634-4386/adcbcb/pdf), gave a presentation on Neuromorphic Computing for prospective computer science students. As we know, the SPICES (Sensorimotor Processing Intelligence and Control in Edge compute Systems) Lab boasts an impressive collection of neuromorphic hardware (https://cristianaxenie.info/spiceslab/) that is waiting to be explored:



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Frangipani

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View attachment 88347




Cloud based AI vs Edge AI Topology

Cloud based AI vs Edge AI Topology

Connected, Low-power, Intelligent Devices at the Edge​


HaiLa

4,385 followers

July 10, 2025
Article by Patricia Bower, HaiLa VP Product

What’s the ‘Edge’ in Edge AI?​

The amount of data generated by billions of wireless connected devices—such as sensors, wearables, and smart appliances—is growing at an incredible rate. Analysts forecast global growth from 20 billion devices today to over 40 billion by 2030 (IoT Analytics, State of IoT 2024).

Using today’s communications networks, user data is sent from (for example) the smartwatch on your wrist to datacenters located miles from you or even across a continent. The data is processed in these facilities by clusters of artificial intelligence (AI) or machine learning (ML) systems and is generally referred to as cloud compute. There are three issues with this model:

  1. The time it takes for the data to traverse communications networks to the datacenter (latency)
  2. The heavy load on datacenter AI clusters, and resulting power consumption, for processing raw data from billions of devices
  3. The higher potential for personal data privacy breaches

The growing need for faster responses (real-time decision making), reduced loading on datacenters (cloud offload), and improved privacy (anonymized data) has therefore driven the rise of edge AI—where intelligent processing happens directly on the device itself, such as in a sensor or wearable. At the core of this shift are neural processing units (NPUs)—specialized AI processors that enable devices to "think locally” and to rely on optimized AI and ML training models.

Chip technology for datacenter AI processing is extremely energy intense. In these AI clusters, where complex computational tasks - such as climate modelling - are performed, compute operations are measured in exaFLOPS, or a million, trillion floating-point operations per second. Edge AI NPUs can support up to a few teraOPS, or trillion operations per second OPS, and are purpose-built to be extremely power efficient. This is important as many connected devices run on batteries.

What Applications are Driving Edge AI?​

Edge AI is already transforming many industries. Here are some notable applications:

Patient Monitoring
Hospitals and home-care settings are increasingly using AI-powered sensors to continuously monitor vital signs such as heart rate, respiration, and oxygen levels. By analyzing these signals locally, devices can detect early warning signs of health deterioration and alert caregivers—without the need to stream personal health data to the cloud. This approach protects patient privacy and saves bandwidth.

Personal Health Monitoring
Wearable devices like fitness trackers and smartwatches are incorporating edge AI to offer more intelligent insights into sleep patterns, stress levels, and activity trends. These devices process sensor data in real time, providing instant feedback and recommendations on the go while preserving battery life.

Retail
In retail environments, smart cameras and sensors are used for foot traffic analysis, shelf inventory monitoring, and customer behavior tracking. Edge AI allows these insights to be processed locally without storing or transmitting images, which supports both privacy and efficiency. For example, a store can detect when a shelf is empty and trigger restocking alerts automatically. Using a battery powered, modular solution with integrated, low-power Wi-Fi provides a simple installation into legacy environments.

Industrial Asset Monitoring and Anomaly Detection
Factories and industrial sites rely on sensors to monitor machinery. Edge AI enables these sensors to learn the normal operating patterns of equipment and detect anomalies like unusual vibrations or temperature spikes—early signs of failure. This allows for predictive maintenance, reducing downtime and maintenance costs.

The ability to collect intelligent data directly from edge devices reduces the need for cloud infrastructure and data transmission, which means that edge AI also lowers the overall cost of system deployment and operation.

Leveraging Low-power RF and Neuromorphic AI for Efficient Edge AI​

A major technical challenge in deploying edge AI is power consumption. Devices like health monitors or industrial sensors are often required to run for months or years on small batteries—or in some cases, without batteries at all. As mentioned, NPUs optimized for low power are an essential element to perform AI tasks like image recognition or anomaly detection using minimal energy. To address this need, BrainChip’s Akida™ neuromorphic technology relies on the principle of sparsity for power efficient, event-driven AI compute.

The inferenced data from NPU AI processing is only part of the equation. Devices must also have the ability to connect and collate this intelligent data from multiple devices in a local or personal area network. This is where low-power radio frequency (RF) technologies like Wi-Fi and Bluetooth Low Energy come in and where HaiLa is setting a new paradigm in just how efficient data transmission can be over these protocols. HaiLa’s extreme low power radio communications technology, paired with power-optimized edge compute, allows devices to send and receive inferenced data without draining energy reserves, making them perfect partners for edge AI systems.

HaiLa and BrainChip: Sensors Converge 2025 Connected Edge AI Demo​

At Sensors Converge in Santa Clara this year, HaiLa and BrainChip joined forces to demonstrate object classification via extreme low-power Wi-Fi.

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HaiLa and BrainChip collaborated to showcase very low power Connected Edge AI Object Classification

This hardware prototype includes a camera module to capture object images, BrainChip’s NPU test platform which is pre-trained to recognize objects from a camera image capture, and HaiLa’s BSC2000 extreme low-power Wi-Fi radio chip which transmits the image class and type data via Wi-Fi to display as a simple icon on a dashboard.

The demo illustrates the potential use of connected, edge AI for applications using image recognition and classification in industrial, retail, and medical sectors where cost effective, low latency, and private data connections are key requirements.

The Future of Edge AI: Connected, Low-power, Intelligent Devices Everywhere​

HaiLa’s core specialization in extreme low-power radio technology over standard protocols like Wi-Fi, Bluetooth, and even cellular, delivers one of the critical enablers of pervasive edge AI: extreme low-power data transmission. Together with BrainChip’s innovative edge compute, this opens up a broad range of options for end-users to support energy-efficient, on-device AI.

Edge AI represents a powerful shift in how intelligent systems operate—bringing the power of AI directly to the devices at the heart of the connected world. By combining efficient NPUs with low-power wireless communication, edge AI systems can run independently, securely, and with minimal energy use. As more industries adopt this technology, we can expect smarter, more responsive, and more sustainable solutions for multiple applications.
Contact us to learn more:

info@haila.io
sales@brainchip.com

HaiLa CEO Derek Kuhn is doing an excellent job promoting our company: “the amazing technology from the team at BrainChip”.

Maybe he should consider taking up a side hustle as a BrainChip social media intern? 😉



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jla

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HaiLa CEO Derek Kuhn is doing an excellent job promoting our company: “the amazing technology from the team at BrainChip”.

Maybe he should consider taking up a side hustle as a BrainChip social media intern? 😉



View attachment 88373
God you put some into exploring BRN for us, God Bless you Fragipanl.
 
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