COOL-NPU: Complementary Online Learning Neural Processing Unit

Abstract

The authors propose a complementary online learning neural processing unit (COOL-NPU) to implement a highly accurate and high-energy-efficient online learning system. It reduces the energy consumption by combining the training methods of convolutional neural network (CNN) and spiking neural network (SNN) and eliminates the power overhead due to the redundant weight update by training trigger with SNN gradient. The proposed SNN core reduces the energy consumption of SNN-gradient generation by two-step encoding and reduces inference power by hierarchical cache with lookup table -mode. In addition, it supports neuron-level event-driven backward operation to maximize the effect of the training trigger. Fabricated with Samsung 28-nm CMOS technology, the COOL-NPU achieves 6.94 mJ/frame and 0.73 mAP for object detection, resulting in 47.7% energy reduction with a slight accuracy loss compared to previous state of the art.