A Skyrmion Racetrack Memory based Computing In-memory Architecture for Binary Neural Convolutional Network

A Skyrmion Racetrack Memory (SRM) based Computing In-Memory Architecture (SRM-CIM) was proposed in this paper. Both data and computing operation can be achieved in SRM-CIM. SRM-CIM is used to support convolutional computing in Binary Convolutional Neural Network (BCNN). Experimental results show that SRM-CIM achieves 98.7% and 82% energy reduction when compared with RRAM and SOT-MRAM based counterparts.