A Low-power Neural 3D Rendering Processor with Bio-inspired Visual Perception Core and Hybrid DNN Acceleration

Abstract

This paper presents a low-power neural 3D rendering processor which can support both inference (INF) and training of the deep neural network (DNN). The processor is realized with four key features: 1) bio-inspired visual perception core (VPC), 2) neural engines using hybrid sparsity exploitation, 3) dynamic neural network allocation (DNNA) core with centrifugal-sampling (CS), and 4) hierarchical weight memory (HWM) with input-channel (iCh) pre-fetcher. Thanks to the VPC and the proposed DNN acceleration architecture, it can improve throughput by 4174x and demonstrates> 30 FPS rendering while consuming 133 mW power.