Using Spin-Hall MTJs to Build an Energy-Efficient In-memory Computation Platform

Abstract

We present the Spin Hall Effect (SHE) Computational Random Access Memory (CRAM) for in-memory computation, incorporating considerations at the device, gate, and functional levels. For two specific applications (2-D convolution and neuromorphic digit recognition), we show that SHE-CRAM is 3x faster and has over 4x lower energy than a prior STT-based CRAM implementation, and is over 2000x faster and at least 130x more energy-efficient than state-of-the-art near-memory processing.