ILMPQ : An Intra-Layer Multi-Precision Deep Neural Network Quantization framework for FPGA

• URL: http://arxiv.org/abs/2111.00155v1
• Date: Sat, 30 Oct 2021 03:02:52 GMT
• Title: ILMPQ : An Intra-Layer Multi-Precision Deep Neural Network Quantization framework for FPGA
• Authors: Sung-En Chang, Yanyu Li, Mengshu Sun, Yanzhi Wang, Xue Lin
• Abstract summary: This work targets the commonly used FPGA (field-programmable gate array) devices as the hardware platform for DNN edge computing. We use a quantization method that supports multiple precisions along the intra-layer dimension. We achieve 3.65x speedup in end-to-end inference time on the ImageNet, compared with the fixed-point quantization method.
• Score: 37.780528948703406
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work targets the commonly used FPGA (field-programmable gate array) devices as the hardware platform for DNN edge computing. We focus on DNN quantization as the main model compression technique. The novelty of this work is: We use a quantization method that supports multiple precisions along the intra-layer dimension, while the existing quantization methods apply multi-precision quantization along the inter-layer dimension. The intra-layer multi-precision method can uniform the hardware configurations for different layers to reduce computation overhead and at the same time preserve the model accuracy as the inter-layer approach. Our proposed ILMPQ DNN quantization framework achieves 70.73 Top1 accuracy in ResNet-18 on the ImageNet dataset. We also validate the proposed MSP framework on two FPGA devices i.e., Xilinx XC7Z020 and XC7Z045. We achieve 3.65x speedup in end-to-end inference time on the ImageNet, compared with the fixed-point quantization method.

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