Energy-Efficient Convolutional Neural Network Based on Cellular Neural Network Using Beyond-CMOS Technologies

Abstract

In this article, we perform a uniform benchmarking for the convolutional neural network (CoNN) based on the cellular neural network (CeNN) using a variety of beyond-CMOS technologies. Representative charge-based and spintronic device technologies are implemented to enable energy-efficient CeNN related computations. To alleviate the delay and energy overheads of the fully connected layer, a hybrid spintronic CeNN-based CoNN system is proposed. It is shown that low-power FETs and spintronic devices are promising candidates to implement energy-efficient CoNNs based on CeNNs. Specifically, more than 10× improvement in energy-delay product (EDP) is demonstrated for the systems using spin diffusion-based devices and tunneling FETs compared to their conventional CMOS counterparts.

Keywords

• Beyond-CMOS technology
• cellular neural network (CeNN)
• convolutional neural network (CoNN)
• spintronics
• tunnel FETs (TFETs)