Quantization-aware training allows S4D sequence models to run at much lower precision, cutting estimated hardware costs by up to two orders of magnitude while keeping accuracy.
Hardware Aware Training for Efficient Keyword Spotting on General Purpose and Specialized Hardware
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abstract
Keyword spotting (KWS) provides a critical user interface for many mobile and edge applications, including phones, wearables, and cars. As KWS systems are typically 'always on', maximizing both accuracy and power efficiency are central to their utility. In this work we use hardware aware training (HAT) to build new KWS neural networks based on the Legendre Memory Unit (LMU) that achieve state-of-the-art (SotA) accuracy and low parameter counts. This allows the neural network to run efficiently on standard hardware (212$\mu$W). We also characterize the power requirements of custom designed accelerator hardware that achieves SotA power efficiency of 8.79$\mu$W, beating general purpose low power hardware (a microcontroller) by 24x and special purpose ASICs by 16x.
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cs.LG 1years
2025 1verdicts
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QS4D: Quantization-aware training for efficient hardware deployment of structured state-space sequential models
Quantization-aware training allows S4D sequence models to run at much lower precision, cutting estimated hardware costs by up to two orders of magnitude while keeping accuracy.