A 22nm, 10.8 μ W/15.1 μ W Dual Computing Modes High Power-Performance-Area Efficiency Domained Background Noise Aware Keyword- Spotting Processor.
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IEEE Trans. Circuits Syst., 2020
QCNN Inspired Reconfigurable Keyword Spotting Processor With Hybrid Data-Weight Reuse Methods.
IEEE Access, 2020
Binarized Weight Neural-Network Inspired Ultra-Low Power Speech Recognition Processor with Time-Domain Based Digital-Analog Mixed Approximate Computing.
Proceedings of the IEEE International Symposium on Circuits and Systems, 2020
A Background Noise Self-adaptive VAD Using SNR Prediction Based Precision Dynamic Reconfigurable Approximate Computing.
Proceedings of the GLSVLSI '20: Great Lakes Symposium on VLSI 2020, 2020
An Ultra-Low Power Always-On Keyword Spotting Accelerator Using Quantized Convolutional Neural Network and Voltage-Domain Analog Switching Network-Based Approximate Computing.
IEEE Access, 2019