EERA-KWS: A 163 TOPS/W Always-on Keyword Spotting Accelerator in 28nm CMOS Using Binary Weight Network and Precision Self-Adaptive Approximate Computing. - Bo Liu 019; Zhen Wang 019; Hu Fan; Jing Yang; Bo Liu 026; Wentao Zhu; Lepeng Huang; Yu Gong; Wei Ge; Longxing Shi (IEEE Access, 2019)

An Ultra-Low Power Always-On Keyword Spotting Accelerator Using Quantized Convolutional Neural Network and Voltage-Domain Analog Switching Network-Based Approximate Computing. - Bo Liu 019; Zhen Wang 019; Wentao Zhu; Yuhao Sun; Zeyu Shen 003; Lepeng Huang; Yan Li 056; Yu Gong; Wei Ge (IEEE Access, 2019)

An Ultra-low Power Keyword-Spotting Accelerator Using Circuit-Architecture-System Co-design and Self-adaptive Approximate Computing Based BWN. - Bo Liu 019; Yuhao Sun; Hao Cai 001; Zeyu Shen 003; Yu Gong; Lepeng Huang; Zhen Wang 019 (ACM Great Lakes Symposium on VLSI, 2020)

A Background Noise Self-adaptive VAD Using SNR Prediction Based Precision Dynamic Reconfigurable Approximate Computing. - Bo Liu 019; Yan Li 056; Lepeng Huang; Hao Cai 001; Wentao Zhu; Shisheng Guo; Yu Gong; Zhen Wang 019 (ACM Great Lakes Symposium on VLSI, 2020)

A 1D-CRNN Inspired Reconfigurable Processor for Noise-robust Low-power Keywords Recognition. - Bo Liu 019; Zeyu Shen 003; Lepeng Huang; Yu Gong; Zilong Zhang; Hao Cai 001 (DATE, 2021)