End-to-end acceleration of the YOLO object detection framework on FPGA-only devices

Zhang, Dezheng; Wang, Aibin; Mo, Ruchan; Wang, Dong

doi:10.1007/s00521-023-09078-8

Neural Comput & Applic

2023

DOI: 10.1007/s00521-023-09078-8

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End-to-end acceleration of the YOLO object detection framework on FPGA-only devices

Dezheng Zhang,

Aibin Wang,

Ruchan Mo

et al.

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Cited by 3 publications

References 27 publications

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Research on ZYNQ neural network acceleration method for aluminum surface microdefects

Zhao,

Liu,

Zhang

et al. 2025

Digital Signal Processing

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Research on ZYNQ neural network acceleration method for aluminum surface microdefects

Zhao,

Liu,

Zhang

et al. 2025

Digital Signal Processing

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An FPGA-Based YOLOv5 Accelerator for Real-Time Industrial Vision Applications

Yan,

Zhang,

Wang

2024

Micromachines

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The You Only Look Once (YOLO) object detection network has garnered widespread adoption in various industries, owing to its superior inference speed and robust detection capabilities. This model has proven invaluable in automating production processes such as material processing, machining, and quality inspection. However, as market competition intensifies, there is a constant demand for higher detection speed and accuracy. Current FPGA accelerators based on 8-bit quantization have struggled to meet these increasingly stringent performance requirements. In response, we present a novel 4-bit quantization-based neural network accelerator for the YOLOv5 model, designed to enhance real-time processing capabilities while maintaining high detection accuracy. To achieve effective model compression, we introduce an optimized quantization scheme that reduces the bit-width of the entire YOLO network—including the first layer—to 4 bits, with only a 1.5% degradation in mean Average Precision (mAP). For the hardware implementation, we propose a unified Digital Signal Processor (DSP) packing scheme, coupled with a novel parity adder tree architecture that accommodates the proposed quantization strategies. This approach efficiently reduces on-chip DSP utilization by 50%, offering a significant improvement in performance and resource efficiency. Experimental results show that the industrial object detection system based on the proposed FPGA accelerator achieves a throughput of 808.6 GOPS and an efficiency of 0.49 GOPS/DSP for YOLOv5s on the ZCU102 board, which is 29% higher than a commercial FPGA accelerator design (Xilinx’s Vitis AI).

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LDF-BNN: A Real-Time and High-Accuracy Binary Neural Network Accelerator Based on the Improved BNext

Wan,

Cen,

Zhang

et al. 2024

Micromachines

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Significant progress has been made in industrial defect detection due to the powerful feature extraction capabilities of deep neural networks (DNNs). However, the high computational cost and memory requirement of DNNs pose a great challenge to the deployment of industrial edge-side devices. Although traditional binary neural networks (BNNs) have the advantages of small storage space requirements, high parallel computing capability, and low power consumption, the problem of significant accuracy degradation cannot be ignored. To tackle these challenges, this paper constructs a BNN with layered data fusion mechanism (LDF-BNN) based on BNext. By introducing the above mechanism, it strives to minimize the bandwidth pressure while reducing the loss of accuracy. Furthermore, we have designed an efficient hardware accelerator architecture based on this mechanism, enhancing the performance of high-accuracy BNN models with complex network structures. Additionally, the introduction of multi-storage parallelism alleviates the limitations imposed by the internal transfer rate, thus improving the overall computational efficiency. The experimental results show that our proposed LDF-BNN outperforms other methods in the comprehensive comparison, achieving a high accuracy of 72.23%, an image processing rate of 72.6 frames per second (FPS), and 1826 giga operations per second (GOPs) on the ImageNet dataset. Meanwhile, LDF-BNN can also be well applied to defect detection dataset Mixed WM-38, achieving a high accuracy of 98.70%.

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End-to-end acceleration of the YOLO object detection framework on FPGA-only devices

Cited by 3 publications

References 27 publications

Research on ZYNQ neural network acceleration method for aluminum surface microdefects

Research on ZYNQ neural network acceleration method for aluminum surface microdefects

An FPGA-Based YOLOv5 Accelerator for Real-Time Industrial Vision Applications

LDF-BNN: A Real-Time and High-Accuracy Binary Neural Network Accelerator Based on the Improved BNext

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