DeltaKWS: A 65nm 36nJ/Decision Bio-inspired Temporal-Sparsity-Aware Digital Keyword Spotting IC with 0.6V Near-Threshold SRAM

Abstract

This paper introduces DeltaKWS, to the best of our knowledge, the first $\Delta$RNN-enabled fine-grained temporal sparsity-aware KWS IC for voice-controlled devices. The 65 nm prototype chip features a number of techniques to enhance performance, area, and power efficiencies, specifically: 1) a bio-inspired delta-gated recurrent neural network ($\Delta$RNN) classifier leveraging temporal similarities between neighboring feature vectors extracted from input frames and network hidden states, eliminating unnecessary operations and memory accesses; 2) an IIR BPF-based FEx that leverages mixed-precision quantization, low-cost computing structure and channel selection; 3) a 24 kB 0.6 V near-$V_{\text{TH}}$ weight SRAM that achieves 6.6X lower read power than the foundry-provided SRAM. From chip measurement results, we show that the DeltaKWS achieves an 11/12-class GSCD accuracy of 90.5%/89.5% respectively and energy consumption of 36 nJ/decision in 65 nm CMOS process. At 87% temporal sparsity, computing latency and energy/inference are reduced by 2.4X/3.4X, respectively. The IIR BPF-based FEx, $\Delta$RNN accelerator, and 24 kB near-$V_{\text{TH}}$ SRAM blocks occupy 0.084 mm${}^2$, 0.319 mm${}^2$, and 0.381 mm${}^2$ respectively (0.78 mm${}^2$ in total).

This paper has been accepted for publication in the IEEE Transactions on Circuits and Systems for Artificial Intelligence (TCASAI)

References in corpus (3)

• ReckOn: A 28nm Sub-mm2 Task-Agnostic Spiking Recurrent Neural Network Processor Enabling On-Chip Learning over Second-Long Timescales
• EdgeDRNN: Recurrent Neural Network Accelerator for Edge Inference
• A 23 $\mu$W Keyword Spotting IC with Ring-Oscillator-Based Time-Domain Feature Extraction

Cited by in corpus (1)

• In-Materia Speech Recognition