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Sima Seeks Greener AI

May 19, 2020

Author: Linley Gwennap

By developing a deep-learning accelerator it calls Mosaic, Sima.ai wants to bring a new level of power efficiency to embedded systems that require neural networks. To facilitate deployment, the startup is also designing a chip called the MLSoC that combines Mosaic with standard embedded-processor components. It expects the chip to deliver 50 trillion INT8 operations per second (TOPS) at 5W for a very impressive 10 TOPS/W. Simulations indicate that the MLSoC will achieve leading efficiency on ResNet-50 as well. The company disclosed new details about the project at the recent Linley Spring Processor Conference.

The MLSoC targets advanced surveillance cameras, robotics, automotive cameras, and other high-performance edge applications. The company recently raised $30 million in a round led by Dell Technologies, bringing its total funding to $40 million. With this cash in hand, it expects to tape out its first product late this year; we expect production in 2H21.

For the main CPU, the company is considering either Cortex-A72A or Cortex-A65—both automotive-grade Arm designs—running at about 1GHz. An image processor (ISP) operates at 2.4Gpixels/s, allowing it to handle four video streams of up to 4K resolution at 60fps. The MLSoC also features a safety block that enables customers to meet ISO 26262 ASIL B or D automotive standards. The chip’s unique feature is the deep-learning accelerator, called Mosaic. It employs a new architecture that shifts complexity from the hardware to the software, thus reducing power consumption, but the company withheld details. The architecture is optimized for CNN inference using 8-bit integer (INT8) precision. 

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