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 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)

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)