A 703.4-GOPs/W Binary SegNet Processor With Computing-Near-Memory Architecture for Road Detection

Abstract

This article proposes a deep learning accelerator with computing-near-memory (CNM) architecture for road detection, which is widely used in driving assistance and automotive driving. The work demonstrates an integrated software/hardware co-design approach. At the algorithm level, a 20-layer binary SegNet is developed. At the hardware level, the accelerator has an optimized CNM architecture with massive bit-level parallel processing elements and pipelines for low latency of the critical path.