PowerYOLO: Mixed Precision Model for Hardware Efficient Object Detection with Event Data

TL;DR

PowerYOLO introduces a low-power, high-accuracy object detection system using event-based sensors and 4-bit quantisation, achieving significant memory and computational efficiency improvements over traditional methods.

Contribution

The paper presents a novel mixed precision approach combining event sensors, 4-bit PoT quantisation, and a custom batch normalisation fusion for efficient object detection.

Findings

Achieves nearly 8x reduction in memory complexity.

Attains a state-of-the-art mAP of 0.301 on GEN1 DVS dataset.

Demonstrates high accuracy with low power consumption.

Abstract

The performance of object detection systems in automotive solutions must be as high as possible, with minimal response time and, due to the often battery-powered operation, low energy consumption. When designing such solutions, we therefore face challenges typical for embedded vision systems: the problem of fitting algorithms of high memory and computational complexity into small low-power devices. In this paper we propose PowerYOLO - a mixed precision solution, which targets three essential elements of such application. First, we propose a system based on a Dynamic Vision Sensor (DVS), a novel sensor, that offers low power requirements and operates well in conditions with variable illumination. It is these features that may make event cameras a preferential choice over frame cameras in some applications. Second, to ensure high accuracy and low memory and computational complexity, we propose to use 4-bit width Powers-of-Two (PoT) quantisation for convolution weights of the YOLO detector, with all other parameters quantised linearly. Finally, we embrace from PoT scheme and replace multiplication with bit-shifting to increase the efficiency of hardware acceleration of such solution, with a special convolution-batch normalisation fusion scheme. The use of specific sensor with PoT quantisation and special batch normalisation fusion leads to a unique system with almost 8x reduction in memory complexity and vast computational simplifications, with relation to a standard approach. This efficient system achieves high accuracy of mAP 0.301 on the GEN1 DVS dataset, marking the new state-of-the-art for such compressed model.