SOT-MRAM-based Binary Neural Networks Demonstration for Single Character Recognition

Abstract

With the substantial increase in the amount of data, the mismatch between the processing speed of the hardware and the software results in the ‘Memory Wall’ problem. Processing-in-memory (PIM), in which the compute and memory units are integrated, can avoid frequent data transmission. Binary neural network (BNN) uses binary weights and activations instead of full-precision weights and activations in the convolutional neural network, which reduces computational complexity with minor influence on accuracy. In this paper, we used a one-step operation to write a pair of SOT-MRAM cells and verified the two basic operations in BNN: XNOR and bitcount. Then, we employed an external control circuit with FPGA and accomplished ‘I’ single-character recognition based on vector-matrix multiplication in the SOT-MRAM array.