A 141 UW, 2.46 PJ/Neuron Binarized Convolutional Neural Network Based Self-Learning Speech Recognition Processor in 28NM CMOS

Abstract

An ultra-low power speech recognition processor is implemented in 28 nm CMOS technology, which is based on an optimized binary convolutional neural network (BCNN). A tailored self-learning mechanism is implemented to learn the features of users and improve recognition accuracy on the fly. Measurement results show that this processor supports real time speech recognition with power consumption of 141 uW and energy efficiency of 2.46 pJ/Neuron when working at 2.5 MHz, while achieving at most 98.6% recognition accuracy.