A PVT-Resilient Subthreshold SRAM-Based In-Memory Computing Accelerator with In-Situ Regulation for Energy-Efficient Spiking Neural Networks

TL;DR

This paper introduces a PVT-resilient, energy-efficient SRAM-based in-memory computing accelerator for spiking neural networks, featuring in-situ regulation and robustness against process variations.

Contribution

It presents a novel subthreshold CIM macro with in-situ sensors and voltage regulators, improving energy efficiency and PVT robustness for large-scale SNNs.

Findings

Achieves 93.64% accuracy on keyword spotting.

Delivers up to 1181.42 TOPS/W energy efficiency.

Demonstrates high variation tolerance and robustness.

Abstract

This paper presents a PVT-resilient, subthreshold SRAM-based computing-in-memory (CIM) macro tailored for energy-efficient spiking neural networks (SNNs). The macro integrates in-situ current sensors and distributed voltage regulators to enable robust large-scale (1024 wordlines, 1304 bitlines and 128 shared neuron cells) subthreshold current-mode CIM, mitigating energy overheads and process-voltage-temperature (PVT) sensitivity. The neuron cells adopt a programmable, memory cell-based firing threshold to enhance neuron robustness against PVT variations. The architecture uses a stride-tick batching schedule to significantly reduce buffer overhead with enhanced input data reuse. Exploiting the high sparsity of SNNs, the proposed system demonstrates significant improvements in energy efficiency and variation tolerance. Fabricated in 28-nm CMOS, the prototype attains 93.64\% accuracy on keyword spotting, delivers up to 1181.42 TOPS/W, and achieves 7.24 TOPS/mm^2, demonstrating a viable and efficient solution for high-performance edge SNN processing.