An accuracy reservation method for quantized inference of neural network on FPGA platform

Bo Lei; Yin Xu; Hai Tan

doi:10.1117/12.3023757

18 March 2024 An accuracy reservation method for quantized inference of neural network on FPGA platform

Bo Lei, Yin Xu, Hai Tan

Proceedings Volume 13104, Advanced Fiber Laser Conference (AFL2023); 1310469 (2024) https://doi.org/10.1117/12.3023757
Event: Advanced Fiber Laser Conference (AFL2023), 2023, Shenzhen, China

Abstract

Most of the edge devices have restricted computational resource, such as ASIC, FPGA or other embedded systems, which cause an efficient problem for neural network model to run in these hardware platform. Model quantization is an effective optimization technique for convolutional layer inference of neural network at the cost of little accuracy loss. However, most of quantized methods only accelerate the computation of convolutional layer, other layers of a model are still inferred by floating-point calculation. FPGA is not an applicable platform for floating-point calculation. In this paper, a completely quantized method is proposed for inference of neural network on FPGA platform. All the calculation of a model inference is performed by quantized value. More quantization leads to more accuracy loss. In order to preserve accuracy, several techniques are used for different functional layer of the neural model. Such as activation layer uses bitwise operation instead of mutilation, concatenate layer use respective parameter for different input layer. To evaluate the effectiveness and efficiency of the proposed method, we implement a quantized light weight detection network, and deploying it on FPGA platform. The experimental result demonstrates that our quantized method is a very low accuracy loss method and is high efficient for neural network inference on FPGA platform. The proposed quantized inference method is highly beneficial for neural model to deploy on low power consumption devices.

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

Citation Download Citation

Bo Lei, Yin Xu, and Hai Tan "An accuracy reservation method for quantized inference of neural network on FPGA platform", Proc. SPIE 13104, Advanced Fiber Laser Conference (AFL2023), 1310469 (18 March 2024); https://doi.org/10.1117/12.3023757

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KEYWORDS

Field programmable gate arrays

Neural networks

Quantization

Convolution

Electrooptical modeling

Object detection

Performance modeling

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