PowerYOLO: Mixed Precision Model for Hardware Efficient Object Detection with Event Data
<|reference_start|>PowerYOLO: Mixed Precision Model for Hardware Efficient Object Detection with Event Data: 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.<|reference_end|>
@article{przewlocka-rus2024poweryolo:,
    title={PowerYOLO: Mixed Precision Model for Hardware Efficient Object Detection
  with Event Data},
    author={Dominika Przewlocka-Rus, Tomasz Kryjak, Marek Gorgon},
    journal={arXiv preprint arXiv:2407.08272},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.08272},
    primaryClass={cs.CV eess.IV}
}