BRN Discussion Ongoing
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Have a look at this Joe and tell me what you think âŚ
If you ask me Joe , I think we have another internal systems issue with JPMorgan with their lending and borrowing legal requirements . Looks like they maybe using a yellow post it note system internally .
Not sure who made the decision to go above 4.99% , but thank you .
All In my opinion âŚâŚ.
#generativeai #edgecomputing #neuralnetworks | BrainChip
đ A huge thank you to all who tuned into the Generative Edge AI Livestream by the EDGE AI FOUNDATION! Our Solutions Architect, Ritik Shrivastava, showcased how BrainChip is leveraging state space models and Akida for real-time generative AI on the edge, all while conserving power and memoryđđĄ...
I missed this funny posit
www.linkedin.com
#dlr #simi #fortiss | BrainChip
Neuromorphic computing is leveling up! The fortiss x neuroTUM Munich Neuromorphic Hackathon pulled together real industry challenges from #DLR, #Simi GmbH, and #fortiss, giving participants five days to build high impact solutions in drone detection, gait identification, and anomaly detection...
Pom down under
Top 20
Come on mate youâve got your point over 86669 times canât you just drop it nowSo⌠no 24 cents?
Pom down under
Top 20
smoothsailing18
Regular
Go brainchip
Mt09
Regular
And then..
Stable Genius
Regular
WBT talking Neuromorphic:
www.linkedin.com
No mention of Brainchip
#neuromorphic #ai #reram #in #imc #neuromorphic #semiconductors #nvm #embeddedworld #rram #ip #embedded #technology | Weebit Nano Ltd
đRead our new article: https://lnkd.in/d5pNdpAt This new blog article by Weebit Nano Ltd Strategic Advisor Gideon Intrater discusses how #neuromorphic-computing can radically improve #AI performance for always-on, real-time applications by mimicking the brainâs parallel, event-driven...
No mention of Brainchip
Yes, I can⌠I wouldâve done that anyway.
But Iâll remind you ..and everyone else .. next time youâre in an argument because someone insulted you, etc.
Itâs always âthe othersâ that bother you when they defend themselves, but when it hits you personally, you think you can take every right for yourself. Anyway
So⌠no 24 cents?
Did todayâs announcement help you work out part of the answer ? Maybe it will help if you can attempt to decipher todayâs messaging without any additional clues .
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Investig8ed
Regular
Has anyone participated in the CR through Australian Super, I'm having difficulty using the app. Any assistance would be great full.
Why participate in the CR when cheaper to purchase at .17 cent?
In my opinion, the only reason you would do this is to help BRN raise capital. By buying on market, BRN doesnât get your money, the person selling obviously does. If you buy through the share offer and BRN sell all $2m worth, then they have an extra $2m in the bank potentially delaying the next capital raise in the futureâŚ. Thatâs allâŚ.
So we can keep Sean for another 7 years
Frangipani
Top 20
#brainchip #globalfoundries #edgeai #embeddedai #aiot #wearables #semiconductors #neuromorphicai | BrainChip
Embedded AI only works when intelligence fits inside strict power and battery limits⥠In our latest blog, Anand Rangarajan from GlobalFoundries and Todd Vierra from BrainChip explore how âright sizingâ AI plus GlobalFoundries 22FDX and BrainChipâs AKD1500 neuromorphic co-processor bring...
Right Sizing AI for Embedded Applications
Right Sizing AI for Embedded Applications By Anand Rangarajan Director, End Markets, GlobalFoundries By Todd Vierra Vice President, Customer Engagement, BrainChip We all know the AI revolution train is heading straight for the Embedded Station. Some of us are already in the driverâs seat...
brainchip.com
Right Sizing AI for Embedded Applications
By Anand Rangarajan
Director, End Markets, GlobalFoundries
By Todd Vierra
Vice President, Customer Engagement, BrainChip
We all know the AI revolution train is heading straight for the Embedded Station. Some of us are already in the driverâs seat, while others are waiting for the first movers to pave the way so we can become fast adopters. No matter where you are on this journey, one thing becomes clear: AI must adapt to the embedded application sandboxânot the other way around.
Embedded applications typically operate within a power envelope ranging from milliwatts to around 10 watts. For AI to be effective in many embedded markets, it must respect the power-performance boundaries of the application. Imagine your favorite device that you charge once a day. If adding embedded AI to a product means you now need to charge it every four hours, you are likely to stop using the product altogether.
This is where embedded AI fundamentally differs from cloud AI. In the cloud, adding more computations is often the default solution. But in embedded systems, the level of AI compute must be dictated by what the overall power and performance constraints allow. You canât just throw more compute silicon at the problem.
There are two key approaches to scaling AI effectively for embedded applications:
1. Process Technology
At the foundational level, advanced process technologies like GlobalFoundriesâ 22FDX+ with Adaptive Body Biasing offer a compelling solution. These transistors can deliver high performance during compute-intensive tasks while maintaining low leakage during idle or always-on modes. This dynamic adaptability ensures that the overall power-performance integrity of the application is preserved.
2. Alternative Compute Architectures
Emerging architectures like neuromorphic computing are gaining attention for their ability to run inference at a fraction of the powerâand with lower latencyâcompared to traditional models. These ultra-low-power solutions are particularly promising for applications where energy efficiency is paramount and real-time response is also important.
BrainChipâs AKD1500 Edge AI co-processor, built on GlobalFoundries 22FDX platform, demonstrates how neuromorphic design can make AI practical for the smallest and most power-sensitive devices. Powered by the companyâs AkidaTM technology, the chip uses an event-based approach, processing only when thereâs information thereby avoiding the constant compute cycles that waste energy by reading and writing to either on-chip SRAM or off-chip DRAM as in traditional AI systems. The co-processor performs event-based convolutions that leverage sparsity throughout the whole network in activation maps and kernels, significantly reducing computation power and latency by running as many layers on the AkidaTM fabric. The diagram below shows all the interfaces, as well as the 8 Node Akida IP as the centerpiece of the AI co-processor.
The design further improves efficiency by handling data locally and using operations that cut power consumption dramatically. The result is a chip that delivers real-time intelligence while operating within just a few hundred milliwatts, making it possible to add AI features to wearable, sensors, and other AIoT devices that previously relied on the cloud for such capability.
The Akida low-cost, low-power AI co-processor solution offers a silicon-proven design that has already demonstrated critical performance metrics, substantially reducing risk for developers. With fully functional interfaces tested at operational speeds and proven interoperability across multiple MCU and MPU boards, the platform ensures seamless integration. The AKD1500 co-processor supports both power-conscious MCUs via SPI4 and high-performance MPUs through M.2 and PCIe interfaces, providing flexibility across many configurations. Enabling software development early with silicon prototypes accelerates time to market. Several customers have already advanced to prototype stages, validating the designâs maturity and readiness for deployment. As an example, Onsor Technologiesâ Nexa smart glasses utilize the AKD1500 for low power inference to predict epileptic seizures, providing quality-of-life benefits for those suffering from epilepsy.
The best part of this is that the AKD1500 can be used with any low cost existing MCU with a SPI interface or an Applications processor where there is a PCIe connection available for higher performance. Adding the AKD1500 AI co-processor makes the time to market very short with available MCUs today.
Final Thoughts
As AI starts to sweep across the length and breadth of embedded space , right sizing becomes not just a technical necessity but a strategic imperative. The goal isnât to fit the biggest model into the smallest device â itâs to fit the right model into the right device, with the right balance of performance, power, and user experience.
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