# ILMPQ : An Intra-Layer Multi-Precision Deep Neural Network Quantization framework for FPGA Source: [ILMPQ : An Intra-Layer Multi-Precision Deep Neural Network Quantization framework for FPGA]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:42:15.448Z Authors: Sung-En Chang, Yanyu Li, Mengshu Sun, Yanzhi Wang, Xue Lin Published: 2021-10-30T03:02:52.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()