The growing popularity of Convolutional Neural Networks (CNNs) has led to the search for efficient computational platforms to enable these algorithms. Resistive random-access memory (ReRAM)-based architectures offer a promising alternative to commonly used GPU-based platforms for CNN training. However, backpropagation in CNNs is susceptible to the limited precision of ReRAMs. As a result, training CNNs on ReRAMs affects the final accuracy of learned model. In this work, we propose REGENT, a heterogeneous architecture that combines ReRAM arrays with GPU cores, and exploits the benefits provided by 3D integration along with a high-throughput yet energy efficient Network-on-Chip (NoC) for training CNNs. We also propose a bin-packing based framework that maps CNN layers and then optimize the placement of computing elements to meet the targeted design objectives. Experimental evaluations indicate that REGENT improves full-system EDP by 55.7% on average compared to conventional GPU-only platforms for training CNNs.
REGENT: A Heterogeneous ReRAM/GPU-based Architecture Enabled by NoC for Training CNNs
Abstract
DOI
10.23919/DATE.2019.8714802
Year