A System-Level Solution for Low-Power Object Detection

TL;DR

This paper proposes a system-level approach combining low-bit quantization, a dedicated accelerator, and a hybrid dataflow to enable efficient, real-time object detection on embedded devices, achieving high speed and accuracy with low power consumption.

Contribution

It introduces a novel system architecture with low-bit quantization and a programmable accelerator for efficient object detection on embedded hardware.

Findings

Achieves 18 fps inference speed on a surveillance video

Operates at 6.9W power consumption

Attains 66.4 mAP on PASCAL VOC 2012 dataset

Abstract

Object detection has made impressive progress in recent years with the help of deep learning. However, state-of-the-art algorithms are both computation and memory intensive. Though many lightweight networks are developed for a trade-off between accuracy and efficiency, it is still a challenge to make it practical on an embedded device. In this paper, we present a system-level solution for efficient object detection on a heterogeneous embedded device. The detection network is quantized to low bits and allows efficient implementation with shift operators. In order to make the most of the benefits of low-bit quantization, we design a dedicated accelerator with programmable logic. Inside the accelerator, a hybrid dataflow is exploited according to the heterogeneous property of different convolutional layers. We adopt a straightforward but resource-friendly column-prior tiling strategy to map the computation-intensive convolutional layers to the accelerator that can support arbitrary feature size. Other operations can be performed on the low-power CPU cores, and the entire system is executed in a pipelined manner. As a case study, we evaluate our object detection system on a real-world surveillance video with input size of 512x512, and it turns out that the system can achieve an inference speed of 18 fps at the cost of 6.9W (with display) with an mAP of 66.4 verified on the PASCAL VOC 2012 dataset.