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.

翻译：本文提出了一种专为高能效脉冲神经网络（SNN）设计的、具有抗PVT（工艺-电压-温度）波动能力的亚阈值SRAM存内计算（CIM）宏单元。该宏单元集成了原位电流传感器和分布式电压调节器，实现了稳健的大规模（1024字线、1304位线和128个共享神经元单元）亚阈值电流模式存内计算，从而降低了能量开销并抑制了工艺-电压-温度（PVT）敏感性。神经元单元采用可编程的、基于存储单元的发放阈值，以增强神经元对PVT变化的鲁棒性。该架构采用步进-滴答批处理调度策略，通过增强输入数据复用显著减少了缓冲器开销。利用SNN的高稀疏性，所提出的系统在能效和变异容忍度方面展现出显著提升。该原型采用28纳米CMOS工艺制造，在关键词识别任务上达到93.64%的精度，能效高达1181.42 TOPS/W，且面积效率达到7.24 TOPS/mm²，为高性能边缘SNN处理提供了一种可行且高效的解决方案。