A Low Power Hardware Implementation of Multi-Object DPM Detector for Autonomous Driving

Abstract

Object detection is a fundamental process in traffic management systems and self-driving cars. Deformable part model (DPM) is a popular and competitive detector for its high precision. This paper presents a programmable, low power hardware implementation of DPM based object detection for real-time applications. Our approach employs a very fast object detection pipeline with complementary techniques such as fast feature pyramid, Fast Fourier Transform (FFT) and early classification to accelerate DPM with a reasonable accuracy loss and achieves a speed-up of 50x and 6x over original DPM and cascade DPM respectively on single core CPU. The hardware circuit uses 65nm CMOS technology and consumes only 36.5mW (0.81 nJ/pixel) based on the post-layout simulation. The ASIC has an area of 3362 kgates and 295.5 KB on-chip memory and the design utilizes two simultaneous engines to process two independent object categories with 8 deformable parts per category.