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[bsfp-cryptocurrency style=”widget-18″ align=”marquee” columns=”6″ coins=”selected” coins-count=”6″ coins-selected=”BTC,ETH,XRP,LTC,EOS,ADA,XLM,NEO,LTC,EOS,XEM,DASH,USDT,BNB,QTUM,XVG,ONT,ZEC,STEEM” currency=”USD” title=”Cryptocurrency Widget” show_title=”0″ icon=”” scheme=”light” bs-show-desktop=”1″ bs-show-tablet=”1″ bs-show-phone=”1″ custom-css-class=”” custom-id=”” css=”.vc_custom_1523079266073{margin-bottom: 0px !important;padding-top: 0px !important;padding-bottom: 0px !important;}”] Introduces MLSoC – First Machine Learning Platform to Break 1000 FPS/W Barrier with 10-30x Improvement over Alternative Solutions

Company Sets out to Enable High Performance Machine Learning to Go Green in Embedded Edge Applications, the company enabling high performance machine learning to go green, announced its Machine Learning SoC (MLSoC) platform – the industry’s first unified solution to support traditional compute with high performance, lowest power, safe and secure machine learning inference. Delivering the highest frames per second per watt,’s MLSoC is the first machine learning platform to break the 1000 FPS/W barrier for ResNet-501. In customer engagements, the company has demonstrated 10-30x improvement in FPS/W through its automated software flow across a wide range of embedded edge applications, over today’s competing solutions. The platform will provide machine learning solutions that range from 50 TOPs@5W to 200 TOPs@20W, delivering an industry first of 10 TOPs/W for high performance inference.

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“The future of compute is high performance machine learning at the edge, and today power is the limiter,” said Krishna Rangasayee, founder and CEO of “We’re delighted to introduce our MLSoC platform, a solution that will accelerate the proliferation of high performance machine learning inference at very low power in embedded edge applications. MLSoC is already demonstrating a 10-30x improvement in FPS/W over alternative solutions in a wide range of applications today. Our very talented team has created a disruptive architecture that is enabling us to deliver push-button results in early customer engagements through our automated software flow. It’s time for machine learning to go green.”

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