Endpoint AI in a Matter of Microwatts

Eta Compute Announces Production Silicon of the World’s Most Energy-efficient Edge AI Processor

Software platform and Processor are ideal for artificial intelligence in edge devices with environmental, sound, motion and image sensors.

WESTLAKE VILLAGE, Calif., February 12, 2020 – Eta Compute Inc., a company dedicated to delivering machine learning to mobile and edge devices using its revolutionary new platform, announces the first shipment of production silicon for its ECM3532, the world’s first AI multicore processor for embedded sensor applications. This unique multicore device features the company’s patented Continuous Voltage Frequency Scaling (CVFS) and delivers power consumption of microwatts for many sensing applications.

Eta Compute’s ECM3532 is a Neural Sensor Processor (NSP) for always-on image and sensor applications. It will be on display at the 2020 tinyML Summit, February 12-13 at Samsung Electronics in San Jose, California. Eta Compute is a Gold Sponsor of tinyML and will demonstrate the ECM3532 for image recognition and other edge sensing applications.  The objective of the entire tinyML community is to enable ultra-low power machine learning at the network edge. 

“Our Neural Sensor Platform is a complete software and hardware platform that delivers more processing at the lowest power profiles in the industry.  This essentially eliminates battery capacity as a barrier to thousands of IoT consumer and industrial applications,” said Ted Tewksbury, CEO of Eta Compute. “We are excited to see the first of many applications our customers are developing come to market later this year.”

Eta Compute’s ECM3532 family brings AI to edge devices and transforms sensor data into actionable information for voice, activity, gesture, sound, image, temperature, pressure, and bio-metrics applications, among others. The platform solves issues for the most important issues in edge computing: lower response time, increased security, and higher accuracy.

“We believe that power consumption, latency and data generation combined with RF transmission are all factors limiting many sensing applications,” said Jim Feldhan,