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Work in Progress: Mobile or FPGA? A Comprehensive Evaluation on Energy Efficiency and a Unified Optimization Framework

2021-06-16 22:38:04
Geng Yuan, Peiyan Dong, Mengshu Sun, Wei Niu, Zhengang Li, Yuxuan Cai, Jun Liu, Weiwen Jiang, Xue Lin, Bin Ren, Xulong Tang, Yanzhi Wang
arXiv_AI
     
Abstract

Efficient deployment of Deep Neural Networks (DNNs) on edge devices (i.e., FPGAs and mobile platforms) is very challenging, especially under a recent witness of the increasing DNN model size and complexity. Although various optimization approaches have been proven to be effective in many DNNs on edge devices, most state-of-the-art work focuses on ad-hoc optimizations, and there lacks a thorough study to comprehensively reveal the potentials and constraints of different edge devices when considering different optimizations. In this paper, we qualitatively and quantitatively compare the energy-efficiency of FPGA-based and mobile-based DNN executions, and provide detailed analysis.

Abstract (translated)

URL

https://arxiv.org/abs/2106.09166

PDF

https://arxiv.org/pdf/2106.09166.pdf

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