SIMBA: A Skyrmionic In-Memory Binary Neural Network Accelerator

Abstract

Magnetic skyrmions are emerging as potential candidates for next-generation non-volatile memories. In this article, we propose an in-memory binary neural network (BNN) accelerator based on the non-volatile skyrmionic memory, which we call as Skyrmionic In-Memory BNN Accelerator (SIMBA). SIMBA consumes 26.7 mJ of energy and 2.7 ms of latency when running inference on a VGG-like BNN. In addition, SIMBA saves up to 97.07% in energy consumption with $3.73\times $ speedup compared with the other accelerators in the literature at similar inference accuracy. Furthermore, we demonstrate improvements in the performance of SIMBA by optimizing material parameters, such as saturation magnetization, anisotropic energy, and damping ratio. Finally, we show that the inference accuracy of BNNs is robust against the possible stochastic behavior of SIMBA (88.5%±1%).