SAN JOSE, CALIF.--(BUSINESS WIRE)--
SiMa.ai, the company enabling high performance machine learning to go green, today 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, SiMa.ai’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.
SiMa.ai is committed in enabling customers to build green high performance machine learning solutions at the embedded edge, including but not limited to:
“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 SiMa.ai. “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.”
SiMa.ai is the company enabling high performance machine learning to go green. Led by a team of industry experts, the company is committed to delivering the highest frames per second per watt solution in the market to its customers – accelerating the proliferation of high performance machine learning inference at very low power in embedded edge applications. Is your ML Green?™
For more details or to schedule a demonstration under NDA, contact: firstname.lastname@example.org.
1ResNet-50 - 224x224; Batch Size 1 at µS latency
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