The following comment from an experience technology expert was in response to the LinkedIn post of Beainchip’s CTO Dr. Tony Lewis. He does seem greatly impressed:The intersection of state-space models like TENNS with neuromorphic architectures like Akida 2.0 presents a fascinating avenue for truly efficient AI. Processing raw audio signals at 2ms latency is remarkable, especially considering the denoising capabilities you've achieved. It begs the question: how does the inherent parallelism of Akida 2.0 influence the training and performance characteristics of TENNS compared to traditional CPU or GPU architectures? The potential for ultra-low power operation across diverse compute platforms is truly groundbreaking. What specific design choices within Akida 2.0 contribute most significantly to this energy efficiency?
Those on LinkedIn should have a look at Stephen Brightfield as he has started to post on the Ai Hardware Summit. Absolutely no doubt Brainchip is in the TENNS software business:Steve Brightfieldreposted this
Our team at brainchip is doing ground breaking ML work. This paper shows our newest results in amazingly fast, low power and state of the art denoising performance. Best if all we process raw audio signals at 2ms latency. We leverage our own state-space model developed at brainchip called TENNS. This model shows state of the art performance across keyword spotting, llms, eyetracking, and now denoising. Oh and did I mention that we have chip ip that can accelerate Tenns? Tenns can run on any compute platform at ultra low power.. but is lowest power on our Akida 2.0 IP. Come by and see a live demo at the Ai hardware & edge ai summit: https://lnkd.in/gG5ypE6C We are a small team making a big splash! Would love to know what you think.“
The TENNS AKIDA 2.0’does seem to be as has been described previously:
1. A GAME CHANGER
2. A WINNING COMBINATION
My opinion only DYOR
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