A 2.2-$μ$W Keyword Spotting System With Filterbank Learning and Attention-Aware Soft-Threshold Denoising in 28-nm CMOS

Keyword spotting (KWS) is crucial for speech interactions in mobile devices, yet its widespread adoption is constrained by the trade-off between low power and high accuracy in diverse noisy environments. Here, we present an ultra-low-power and robust KWS chip to address this challenge through architecture and circuit co-optimization. First, a learnable Mel filter bank is proposed and co-designed with a group-based time-multiplexed architecture to perform adaptive spectral reshaping in a log-Mel feature extractor, reducing the downstream network computations by 25.8% while preserving feature representation capability. Second, a broadcasted residual network (BC-ResNet) is designed to support both KWS and speaker verification (SV) classification tasks, which reduces additions by 43.5% and parameters by 5% compared to a lightweight depthwise separable CNN (DSCNN). Third, an attention-aware soft-threshold denoising strategy is developed to dynamically filter noisy features, yielding up to a 5.8% increase in accuracy over the baseline without denoising at 0dB signal-to-noise ratio (SNR). Finally, a multiplier-less processing element (PE) array combined with stacked-transistor-based ultra-low-leakage custom memory is designed, reducing hardware area by 31.3% and leakage power by 20.4%. Fabricated in a 28nm CMOS process, the proposed 12-class KWS chip consumes only$2.2\mu $W at 0.42V supply. It achieves 87.6%-93.5% accuracy on GSCD across 0dB to 20dB SNR levels, demonstrating high robustness for diverse scenarios.