Energy Efficient Spin-Based Implementation of Neuromorphic Functions in CNNs

Abstract

Convolutional neural networks (CNNs) offer potentially a better accuracy alternative for conventional deep learning tasks. The hardware implementation of CNN functionalities with conventional CMOS based devices still lags in area and energy efficiency. This has necessitated the investigations of unconventional devices, circuits, and architectures to efficiently mimic the functionality of neurons and synapses for neuromorphic applications. Spin-orbit torque magnetic tunnel junction (SOT-MTJ) device is capable of achieving energy and area efficient rectified linear unit (ReLU) activation functionality. This work utilizes the SOT-MTJ based ReLU for activation and max-pooling in a single unit to eliminate the need of dedicated hardware for pooling layer. Moreover, 2 × 2 multiply-accumulate-activate-pool (MAAP) is implemented by using four activation pairs each of which is fed by the crossbar output. The presented approach has been used to implement various CNN architectures and evaluated for CIFAR-10 image classification. The number of read/write operations reduce significantly by 2X in MAAP based CNN architectures. The results show that the area and energy in MAAP based CNN is improved by at least 25% and 82.9%, respectively, when compared with conventional CNN designs.

Keywords

• Convolutional neural network (CNN)
• spintronics
• spin-orbit torque (SOT)
• multiply-accumulate (MAC)
• magnetic tunnel junction (MTJ)