Year: 2021
Type: preprint
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.
Source: arXiv (Cornell University)
Institution: Northeastern University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: National Science Foundation
Awards: RTML: Large: Efficient and Adaptive Real-Time Learning for Next Generation Wireless Systems, SHF: Medium: Collaborative Research: ADMM-NN: A Unified Software/Hardware Framework of DNN Computation and Storage Reduction Using ADMM, SPX: Collaborative Research: FASTLEAP: FPGA based compact Deep Learning Platform
OpenAlex ID: W3211065149