Work in Progress: Mobile or FPGA? A Comprehensive Evaluation on Energy Efficiency and a Unified Optimization Framework
Geng Yuan, Peiyan Dong, Mengshu Sun, Wei Hua Niu, Zhengang Li, Yuxuan Cai, Jùn Líu, Weiwen Jiang, Xue Lin, Bin Ren, Xulong Tang, Yanzhi Wang · 2021
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 energyefficiency of FPGA-based and mobile-based DNN executions, and provide detailed analysis.