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 1.33x higher throughput than the previous state-of-the-art DNN learning processor. Second, the 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). The BFA and dynamic fixed-point-based RLC reduce the EMA by 11.4% and 32.5%, respectively. Third, the new data feeding units, including an integral RLC (iRLC) decoder and a transpose RLC (tRLC) decoder, are co-designed to maximize throughput alongside the proposed algorithms. Finally, a dropout controller in this processor reduces redundant power consumption coming from the unified core and the data feeding architecture 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 throughput in the object tracking application.