Eta Compute Launches Machine Learning Platform with Ultra-Low-Power Consumption for Edge Devices | Be Korea-savvy

Eta Compute Launches Machine Learning Platform with Ultra-Low-Power Consumption for Edge Devices

Solar powered sensing board with TENSAI® Chip in 48 pin 7X7mm QFN package. (image: Eta Compute)

Solar powered sensing board with TENSAI® Chip in 48 pin 7X7mm QFN package. (image: Eta Compute)


WESTLAKE VILLAGE, Calif., Oct. 16 (Korea Bizwire) — Eta Compute Inc., a company dedicated to delivering machine learning to mobile and edge devices using its revolutionary new platform, today announced the availability of its latest machine learning SoC that includes autonomous learning. Named TENSAI®, this ground-breaking product performs image classification, keyword spotting, and wakeup word detection that redefines the standard for ultra-low power embedded solutions.

“I know machine learning on tiny, cheap battery powered chips is coming,” said Pete Warden, Google Technical Lead of TensorFlow. This will open the door for some amazing new applications.”

The TENSAI chip includes the third generation of Eta Compute’s delay insensitive logic which enables products to reliably operate at the lowest supply voltage resulting in the lowest power consumption.

Other unique features of this SoC include:

  • Eta Compute’s own kernel for spiking neural network (SNN) and CNN minimizes operations and lowers power consumption
  • Autonomous Learning of speech, image, and other data where classification occurs on the data without labels enabling advances in the broad area of anomaly detection on systems where failure modes are unknown or data difficult to obtain
  • Image classification application consuming only 0.4mJ per picture, a 30X power reduction over recently published results
  • Always-on wakeup word application which consumes 500uA during classification or 50uA during silence meeting strict requirements for wearables and battery-operated consumer electronics

“Our patented hardware architecture (DIAL™) is combined with our fully customizable algorithms based on both CNN and SNNs to perform machine learning inferencing in hundreds of microwatts,” said Nara Srinivasa Ph.D., CTO of Eta Compute. “These are being sampled to customers who are integrating them into products such as smart speakers and object detection platforms to deliver machine intelligence to the network edge.”

The processor is trainable using the popular TensorFlow® or Caffe® software and Eta Compute’s custom kernel further optimizes the trained model. TENSAI uses a tightly integrated DSP processor and microcontroller architecture for a significant reduction in power for embedded machine intelligence. This solution can support a wide range of applications in audio, video, and signal processing where power is a severe constraint as in mobile devices, wearable, industrial sensing, and camera markets.

Furthermore, for real world scenarios for which readily labeled data is scarce or unavailable, our autonomous learning algorithms can extract actionable intelligence despite this limitation. This makes Eta Compute’s solution much broader in scope including intelligence for devices that harvest energy in remote environments.

Eta Compute SoC with machine learning is sampling now with mass production expected in Q1 of 2019.

About Eta Compute
Eta Compute was founded in 2015 with the vision that the proliferation of intelligent devices at the network edge will make daily life safer, healthier, comfortable and more convenient without sacrificing privacy and security. Its recently launched DIAL™ technology is the world’s lowest power embedded compute platform and is a natural architecture to support event driven neuromorphic learning and machine intelligence for portable devices. For more information visit or contact the company via email at

Phyllis Grabot, Corridor Communications, Inc.
805.341.7269 /

A photo accompanying this announcement is available at

Source: Eta Compute via GLOBE NEWSWIRE

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