An in-memory computing multiply-and-accumulate circuit based on ternary STT-MRAMs for convolutional neural networks

Abstract

In-memory computing (IMC) quantized neural network (QNN) accelerators are extensively used to improve energy-efficiency. However, ternary neural network (TNN) accelerators with bitwise operations in nonvolatile memory are lacked. In addition, specific accelerators are generally used for a single algorithm with limited applications. In this report, a multiply-and-accumulate (MAC) circuit based on ternary spin-torque transfer magnetic random access memory (STT-MRAM) is proposed, which allows writing, reading, and multiplying operations in memory and accumulations near memory. The design is a promising scheme to implement hybrid binary and ternary neural network accelerators.