Chong, Y. S., Goh, W. L., Nambiar, V. P., & Do, A. T. (2022). A 2.5 μW KWS Engine With Pruned LSTM and Embedded MFCC for IoT Applications. IEEE Transactions on Circuits and Systems II: Express Briefs, 69(3), 1662–1666. https://doi.org/10.1109/tcsii.2021.3113259
Abstract:
Always-on keyword spotting (KWS) hardware is gaining popularity in ultra-low power IoT applications where specific words are used to wake up and activate the power hungry downstream system. This work proposes a low power KWS engine with a power-optimized Mel-frequency cepstral coefficients (MFCC) feature extraction module and a memory-optimized long short term memory (LSTM) accelerator. Our LSTM model is pruned and compressed to reduce 89% of model size and 76% of computation. The LSTM accelerator adopts the weight stationary dataflow to reduce energy. Our simulation using a 40nm CMOS process achieves a power consumption of 2.51 μW , which is at least 2× lower than the state-of-the-art.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research - Advanced Manufacturing and Engineering (AME) - Neuromorphic Processor Design - Neuron Circuits
Grant Reference no. : A1687b0033