A High-Density Energy-Efficient CNM Macro Using Hybrid RRAM and SRAM for Memory-Bound Applications

Abstract

The big data era has facilitated various memory-centric algorithms, such as the Transformer decoder, neural network, stochastic computing (SC), and genetic sequence matching, which impose high demands on memory capacity, bandwidth, and access power consumption. The emerging nonvolatile memory devices and compute-near-memory (CNM) architecture offer a promising solution for memory-bound tasks. This work proposes a hybrid resistive random access memory (RRAM) and static random access memory (SRAM) CNM architecture. The main contributions include: 1) proposing an energy-efficient and high-density CNM architecture based on the hybrid integration of RRAM and SRAM arrays; 2) designing low-power CNM circuits using the logic gates and dynamic-logic adder with configurable datapath; and 3) proposing a broadcast mechanism with output-stationary workflow to reduce memory access. The proposed RRAM-SRAM CNM architecture and dataflow tailored for four distinct applications are evaluated at a 28-nm technology, achieving 4.62-TOPS $/$ W energy efficiency and 1.20-Mb $/$ mm² memory density, which shows $11.35\times $ – $25.81\times $ and $1.44\times $ – $4.92\times $ improvement compared to previous works, respectively.