“This allows the Amazon Echo ecosystem to expand into most battery powered devices, which it couldn’t previously do,” says Syntiant CEO Kurt Busch. “You really couldn’t do always on listening with small battery powered devices, and now you can.” Sanjay Voleti, senior manager of device enablement with Alexa Voice Service was fascinated with Syntiant’s solution. “We’re excited to see developers begin using this technology in their devices and deliver new Alexa experiences for customers,” he said in a press release.

 

The chip attaches straight to a digital microphone or other sensor and initiates an “interrupt” line connecting to the larger—usually sleeping—system. Once that system’s yawned and expanded, it can then interrogate the NDP100 to determine what wake word or command it heard. The chip also keeps a three-second audio buffer if perhaps the system needs to catch up on what was said during its wakeup routine.

 

Like most AI ASICs, the chip only performs the inferencing step of deep learning. The training happens in the cloud using the common TensorFlow software library. Just one among the chip’s biggest advantages for developers, according to Busch, is that it doesn’t need any compiling or optimization step between the trained network and what goes on the chip. “It’s fair to say that the guts of our chip look like the Tensor Flow graph,” says Busch. Busch says Syntiant is now operating on a larger, more capable version of the chip to expand into more markets. It is also thriving a version that uses analog computation and embedded flash memory to do AI computations with less power.

 

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