A Nonvolatile Compute-in-Memory Macro Using Voltage-Controlled MRAM and In Situ Magnetic-to-Digital Converter

Abstract

Compute-in-memory (CIM) accelerator has become a popular solution to achieve high energy efficiency for deep learning applications in edge devices. Recent works have demonstrated CIM macros using nonvolatile memories [spin transfer torque (STT)-MRAM and resistive random access memory (RRAM)] to take advantages of their nonvolatility and high density. However, effective computation dynamic range is far lower than their static random access memory (SRAM)-CIM counterparts due to low device ON/ OFF ratio. In this work, we combine a nonvolatile memory based on a voltage-controlled magnetic tunneling junction (VC-MTJ) device, called voltage-controlled MRAM or VC-MRAM, and accurate switched-capacitor-based CIM using a novel in situ magnetic-to-digital converter (MDC). The VC-MTJ device has demonstrated $10\times $ lower write energy and switching time compared to STT-MRAM device and has comparable density, read energy, and read latency. The in situ MDCs embedded inside each VC-MRAM row convert magnetically stored weight information to CMOS logic levels and enable switched-capacitor-based multiply–accumulate (MAC) operation with accuracy comparable to the state-of-the-art SRAM-CIM. This article describes the schematic and layout level design of a VC-MRAM CIM macro in 28 nm. This is the first nonvolatile CIM design to enable analog MAC computation with 256 parallel rows turned ON simultaneously without degradation in dynamic range (< 1 LSB). Detailed circuit simulations including experimentally validated VC-MTJ compact models show $1.5\times $ higher energy efficiency and $2\times $ higher density compared to the state-of-the-art SRAM-based CIM.

Keywords

• Compute-in-memory (CIM)
• deep learning accelerator
• nonvolatile memory
• voltage-control MRAM