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Syntiant Knows All the Best Words

March 19, 2019

Author: Mike Demler

Syntiant, a neural-network-processor startup, publicly touts analog in-memory computing as its raison d’etre, but its first products implement an entirely digital architecture. Potential customers expressed interest in the company’s low-power digital prototype, so it decided to build a production version. The NDP100 and NDP101 use digital multiply-accumulate (MAC) units rather than the flash-memory-based multipliers that Syntiant plans to use in its analog design. The NDP10x targets always-on near-field keyword and speaker recognition in battery-powered devices, including earbuds, headsets, remote controls, and other voice-activated products. It can also recognize other acoustic events, such as broken glass and gunshots. The company manufactures the chips in a 40nm ULP process. It delivered first samples in July 2018 and expects to ship production volumes in 2Q19.

During active operation, the NDP10x consumes less than 200 microwatts, but Syntiant expects that replacing the digital MACs with its analog technology will deliver additional power savings. The inference engine handles 4-bit weights, which require half the power to compute, move, and store data compared with the more common INT8 data type. The NDP10x provides 2.0 trillion operations per watt. It processes speech divided into a maximum 200 frames per second, using 560,000 MAC operations per frame, for a total of 240 million operations per second (MOPS). Using Syntiant’s Python-based training-development kit (TDK), customers can train the NDP10x to recognize up to 64 words. The inference engine can classify 100 words per second.

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