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

• Main Authors: Chenyun Pan, Qiuwen Lou, Michael Niemier, Sharon Hu, Azad Naeemi
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
• Subjects: Beyond-CMOS technology; cellular neural network (CeNN); convolutional neural network (CoNN); spintronics; tunnel FETs (TFETs)
• Online Access: https://ieeexplore.ieee.org/document/8935350/
• ISSN: 2329-9231

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.