(A) low-power deep neural network online learning processor for real-time object tracking application

Abstract

A deep neural network (DNN) online learning processor is proposed with high throughput and low power consumption to achieve real-time object tracking in mobile devices. Four key features enable a low-power DNN online learning. First, a proposed processor is designed with a unified core architecture and it achieves 1.33 × higher throughput than the previous state-of-the-art DNN learning processor. Second, new algorithms, binary feedback alignment (BFA) and dynamic fixed-point based run-length compression (RLC), are proposed and reduce power consumption through the reduction of external memory accesses (EMA). Third, new data feeding units, including an integral RLC (iRLC) decoder and a transpose RLC (tRLC) decoder, are co-designed to maximize throughput alongside with the proposed algorithms. Finally, a dropout controller in this processor reduces redundant power consumption by the proposed dynamic clock-gating scheme. This enables the proposed processor to operate DNN online learning with 38.1% lower power consumption. Implemented with 65 nm CMOS technology, the 3.52$mm^2$ DNN online learning processor shows 126 mW power consumption and the processor achieves 30.4 frames-per-second (FPS) throughput in the object tracking application