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)

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)

Binarized Weight Neural-Network Inspired Ultra-Low Power Speech Recognition Processor with Time-Domain Based Digital-Analog Mixed Approximate Computing. - Bo Liu 019; Hao Cai 001; Yu Gong; Wentao Zhu; Yan Li 056; Wei Ge; Zhen Wang 019 (ISCAS, 2020)

QCNN Inspired Reconfigurable Keyword Spotting Processor With Hybrid Data-Weight Reuse Methods. - Yu Gong; Yan Li 056; Xiaoling Ding; Haichuan Yang; Zilong Zhang; Xuan Zhang; Wei Ge; Zhen Wang 019; Bo Liu 019 (IEEE Access, 2020)

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. - Bo Liu 019; Hao Cai 001; Zhen Wang 019; Yuhao Sun; Zeyu Shen 003; Wentao Zhu; Yan Li 056; Yu Gong; Wei Ge; Jun Yang 006; Longxing Shi (IEEE Trans. Circuits Syst., 2020)