An Ultra-low Power Keyword-Spotting Accelerator Using Circuit-Architecture-System Co-design and Self-adaptive Approximate Computing Based BWN

This paper proposed an ultra-low power keyword-spotting (KWS) accelerator using circuit-architecture-system co-design and precision self-adaptive approximate computing based binarized weight network (BWN). To reduce the power consumption while maintaining the system recognition accuracy for different background noise, we first proposed a bit-by-bit layer-by-layer quantization method to quantize the deep neural network (DNN) to BWN. Then, we proposed a precision self-adaptive approximate addition unit to further reduce the BWN energy consumption. Evaluated under TSMC22nm ULL process technology, this work can support up to 10 keywords real time recognition under different background noise types and SNRs (from 5dB to near microphone) with power consumption of 13.6uW.