A 1.32 TOPS/W Energy Efficient Deep Neural Network Learning Processor with Direct Feedback Alignment based Heterogeneous Core Architecture

Abstract

An energy efficient deep neural network (DNN) learning processor is proposed using direct feedback alignment (DFA). The proposed processor achieves $2.2 \times$ faster learning speed compared with the previous learning processors by the pipelined DFA (PDFA). In order to enhance the energy efficiency by 38.7%, the heterogeneous learning core (LC) architecture is optimized with the 11-stage pipeline data-path. Furthermore, direct error propagation core (DEPC) utilizes random number generators (RNG) to remove external memory access (EMA) caused by error propagation (EP) and improve the energy efficiency by 19.9%. The proposed PDFA based learning processor is evaluated on the object tracking (OT) application, and as a result, it shows 34.4 frames-per-second (FPS) throughput with 1.32 TOPS/W energy efficiency.