Performance Prospects of Deeply Scaled Spin-Transfer Torque Magnetic Random-Access Memory for In-Memory Computing

Abstract

In recent years, Spin-Transfer-Torque Magnetic Random Access Memory (STT-MRAM) has been considered as one of the most promising non-volatile memory candidates for in-memory computing. However, system-level performance gains using STT-MRAM for in-memory computing at deeply scaled nodes have not been assessed with respect to more mature memory technologies. In this letter, we present perpendicular magnetic tunnel junction (pMTJ) STT-MRAM devices at 28nm and 7nm. We evaluate the system-level performance of convolutional neural network (CNN) inference with STT-MRAM arrays in comparison to Static Random Access Memory (SRAM). We benchmark STT-MRAM and SRAM in terms of area, leakage power, energy, and latency from 65nm to 7nm technology nodes. Our results show that STT-MRAM keeps providing $\sim 5\times $ smaller synaptic core area, $\sim 20\times $ less leakage power, and $\sim 7\times $ less energy than SRAM when both devices are scaled from 65nm to 7nm. With the emerging need for low power computation for a broad range of applications such as internet-of-things (IoT) and neural network (NN), STT-MRAM can offer energy-efficient and high-density in-memory computing.