Hardware accelerator design for object detection based on convolutional neural network

BangXu Li; Kang Wang; ErBo Zou

doi:10.1117/12.3022759

18 March 2024 Hardware accelerator design for object detection based on convolutional neural network

BangXu Li, Kang Wang, ErBo Zou

Proceedings Volume 13104, Advanced Fiber Laser Conference (AFL2023); 1310422 (2024) https://doi.org/10.1117/12.3022759
Event: Advanced Fiber Laser Conference (AFL2023), 2023, Shenzhen, China

Abstract

Most deep learning models for object detection are designed based on convolutional neural networks, requiring powerful computing and storage capabilities typically provided by hardware platforms such as GPUs and CPUs. In contrast, FPGAs offer low power consumption and strong computational capabilities; however, deploying neural network models directly on FPGA embedded platforms is challenging. To address these issues, this paper takes the YOLO-V3 target detection algorithm as an example, introducing the hierarchical structure of the YOLO-V3 network, analyzing acceleration methods for each layer in the YOLO-V3 network, designing a convolutional neural network accelerator, and comparing its performance with that of GPUs. The designed accelerator effectively utilizes FPGA hardware computing resources, achieving an overall average performance of 192.229 GOP/s.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

BangXu Li, Kang Wang, and ErBo Zou "Hardware accelerator design for object detection based on convolutional neural network", Proc. SPIE 13104, Advanced Fiber Laser Conference (AFL2023), 1310422 (18 March 2024); https://doi.org/10.1117/12.3022759

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