An Asynchronous Analog-Computing Spiking Neural Network With Improved Tolerance to Nonidealities for Always-On Near-Sensor AI

Spiking Neural Networks (SNN) is well-suited for always-on near-sensor intelligence, due to its spike-driven nature; however, its IC realization is complicated by the temporal dimension. Asynchronous low-power SNN chips employing analog computing-in-memory (CIM) techniques have been demonstrated to enable real-time, energy-efficient inference. However, their tolerance to nonidealities remains to be improved, and their peripherals for multiphase or multilevel signal control are complex. This paper proposes a general-purpose, spike-driven SNN chip designed with efficient analog-computing circuits, featuring two key contributions: 1) A compact direct current-add CIM synapse design that significantly simplifies control peripherals, thereby reducing latency and enhancing energy efficiency. 2) A PVT-aware multi-network learning method underpinned by detailed analyses and modeling, coupled with a label-normalization-based synapse-strengthening method, to mitigate the impact of nonidealities. Fabricated in 65nm CMOS process, the proposed design achieves a state-of-the-art latency of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10\mu $</tex-math> </inline-formula>s and an energy efficiency of 0.40pJ/spike. The generalization ability of the design is verified through its successful application to two distinct tasks: voice activity detection (VAD) and ECG anomaly detection. The tolerance to nonidealities is validated by the VAD task. The ten chips maintain over 90% detection accuracy across signal-to-noise ratios (SNRs) of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4\sim 16$</tex-math> </inline-formula>dB, ±10% supply voltage variation, and a temperature range of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$- 25\sim 55^{\circ }$</tex-math> </inline-formula>C.