High Performance Training of Deep Neural Networks Using Pipelined Hardware Acceleration and Distributed Memory

Raghav Mehta1, Yuyang Huang1, Mingxi Cheng2, Shrey Bagga1, Nishant Mathur1, Ji Li1, Jeffrey Draper3, Shahin Nazarian1
1University of Southern California, 2Duke University, 3Information Sciences Institute


Recently, Deep Neural Networks (DNNs) have made unprecedented progress in various tasks. However, there is a timely need to accelerate the training process in DNNs specifically for real-time application that demand high performance, energy efficiency and compactness. Numerous algorithms have been proposed to improve the accuracy, however the network training process is computationally slow. In this paper, we present a scalable pipelined hardware architecture with distributed memories for a digital neuron to implement deep neural networks. We also explore various functions and algorithms as well as different memory topologies, to optimize the performance of our training architecture. The power, area, and delay of our proposed model are evaluated with respect to software implementation. Experimental results on the MNIST dataset demonstrate that compared with the software training, our proposed hardwarebased approach for training process achieves 33X runtime reduction, 5X power reduction, and 168X energy reduction.

Keywords—Deep learning, neural network, hardware design.