SSO-LSM: A Sparse and Self-Organizing architecture for Liquid State Machine based neural processors. - Yingyezhe Jin; Yu Liu 028; Peng Li 001 (NANOARCH, 2016)

Exploring sparsity of firing activities and clock gating for energy-efficient recurrent spiking neural processors. - Yu Liu 028; Yingyezhe Jin; Peng Li 001 (ISLPED, 2017)

Design and architectural co-optimization of monolithic 3D liquid state machine-based neuromorphic processor. - Bon Woong Ku; Yu Liu 028; Yingyezhe Jin; Sandeep Kumar Samal; Peng Li 001; Sung Kyu Lim (DAC, 2018)

Area-efficient and low-power face-to-face-bonded 3D liquid state machine design. - Bon Woong Ku; Yu Liu 028; Yingyezhe Jin; Peng Li 001; Sung Kyu Lim (ICCAD, 2018)

Online Adaptation and Energy Minimization for Hardware Recurrent Spiking Neural Networks. - Yu Liu 028; Yingyezhe Jin; Peng Li 001 (ACM J. Emerg. Technol. Comput. Syst., 2018)

Boosting Throughput and Efficiency of Hardware Spiking Neural Accelerators using Time Compression Supporting Multiple Spike Codes. - Changqing Xu; Wenrui Zhang; Yu Liu 028; Peng Li 001 (CoRR, 2019)

Enabling Non-Hebbian Learning in Recurrent Spiking Neural Processors With Hardware-Friendly On-Chip Intrinsic Plasticity. - Yu Liu 028; Wenrui Zhang; Peng Li 001 (IEEE J. Emerg. Sel. Topics Circuits Syst., 2019)

Energy-efficient FPGA Spiking Neural Accelerators with Supervised and Unsupervised Spike-timing-dependent-Plasticity. - Yu Liu 028; Sai Sourabh Yenamachintala; Peng Li 001 (ACM J. Emerg. Technol. Comput. Syst., 2019)