OISMA: On-the-fly In-memory Stochastic Multiplication Architecture for Matrix-Multiplication Workloads

TL;DR

OISMA is an energy-efficient in-memory computing architecture leveraging stochastic multiplication for matrix workloads, demonstrating significant improvements in energy and area efficiency over traditional IMC architectures.

Contribution

This work introduces OISMA, a novel IMC architecture that uses stochastic computing to enhance energy efficiency and scalability for matrix multiplication tasks.

Findings

Achieves 0.789 TOPS/W energy efficiency at 50 MHz.

Demonstrates two orders of magnitude energy efficiency improvement at 22-nm scaling.

Supports matrix multiplication with high area and energy efficiency.

Abstract

Artificial intelligence (AI) models are currently driven by a significant upscaling of their complexity, with massive matrix-multiplication workloads representing the major computational bottleneck. In-memory computing (IMC) architectures are proposed to avoid the von Neumann bottleneck. However, both digital/binary-based and analog IMC architectures suffer from various limitations, which significantly degrade the performance and energy efficiency gains. This work proposes OISMA, an energy-efficient IMC architecture that utilizes the computational simplicity of a quasi-stochastic computing (SC) domain (bent-pyramid (BP) system) while keeping the same efficiency, scalability, and productivity of digital memories. OISMA converts normal memory read operations into in situ stochastic multiplication operations with a negligible cost. An accumulation periphery then accumulates the output multiplication bitstreams, achieving the matrix multiplication (MatMul) functionality. A 4-kB 1T1R OISMA array was implemented using a commercial 180-nm technology node and in-house resistive random-access memory (RRAM) technology. At 50 MHz, it achieves 0.789 TOPS/W and 3.98 GOPS/mm2 for energy and area efficiency, respectively, occupying an effective computing area of 0.804241 mm2. Scaling OISMA to 22-nm technology shows a significant improvement of two orders of magnitude in energy efficiency and one order of magnitude in area efficiency, compared to dense MatMul IMC architectures.