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.
翻译:在边缘装置(即FPGAs和移动平台)上有效部署深神经网络(DNNs)非常困难,特别是最近目睹DNN模型规模和复杂性日益增大的一个实例,尽管事实证明,在许多边缘装置上的DNNs中,各种优化办法是有效的,但大多数最先进的工作侧重于临时优化,而且缺乏全面研究,以全面揭示不同边缘装置在考虑不同优化时的潜力和制约因素。