J
Jing Pei
Researcher at Tsinghua University
Publications - 58
Citations - 1654
Jing Pei is an academic researcher from Tsinghua University. The author has contributed to research in topics: Neuromorphic engineering & Artificial neural network. The author has an hindex of 15, co-authored 49 publications receiving 906 citations.
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Towards artificial general intelligence with hybrid Tianjic chip architecture.
Jing Pei,Lei Deng,Sen Song,Sen Song,Mingguo Zhao,Youhui Zhang,Shuang Wu,Guanrui Wang,Zhe Zou,Zhenzhi Wu,Wei He,Feng Chen,Ning Deng,Si Wu,Yu Wang,Yujie Wu,Z. Yang,Cheng Ma,Guoqi Li,Wentao Han,Huanglong Li,Huaqiang Wu,Rong Zhao,Yuan Xie,Luping Shi +24 more
TL;DR: The Tianjic chip is presented, which integrates neuroscience-oriented and computer-science-oriented approaches to artificial general intelligence to provide a hybrid, synergistic platform and is expected to stimulate AGI development by paving the way to more generalized hardware platforms.
Journal ArticleDOI
GXNOR-Net: Training deep neural networks with ternary weights and activations without full-precision memory under a unified discretization framework.
TL;DR: Gated XNOR-Nets as mentioned in this paper subsume binary and ternary networks as its special cases, and under which a heuristic algorithm is provided at the website https://github.com/AcrossV/Gated-XNOR.
Journal ArticleDOI
A system hierarchy for brain-inspired computing
Youhui Zhang,Peng Qu,Yu Ji,Weihao Zhang,Guangrong Gao,Guanrui Wang,Sen Song,Guoqi Li,Wenguang Chen,Weimin Zheng,Feng Chen,Jing Pei,Zhao Rong,Mingguo Zhao,Luping Shi +14 more
TL;DR: This study proposes 'neuromorphic completeness', which relaxes the requirement for hardware completeness, and proposes a corresponding system hierarchy, which consists of a Turing-complete software-abstraction model and a versatile abstract neuromorphic architecture.
Posted Content
GXNOR-Net: Training deep neural networks with ternary weights and activations without full-precision memory under a unified discretization framework
TL;DR: It is found that when both the weights and activations become ternary values, the DNNs can be reduced to sparse binary networks, termed as gated XNOR networks (GXNOR-Nets), which promises the event-driven hardware design for efficient mobile intelligence.
Journal ArticleDOI
Truly Concomitant and Independently Expressed Short‐ and Long‐Term Plasticity in a Bi 2 O 2 Se‐Based Three‐Terminal Memristor
Ziyang Zhang,Tianran Li,Yujie Wu,Yinjun Jia,Congwei Tan,Xintong Xu,Guanrui Wang,Juan Lv,Wei Zhang,Yuhan He,Jing Pei,Cheng Ma,Guoqi Li,Haizheng Xu,Luping Shi,Hailin Peng,Huanglong Li +16 more
TL;DR: A heuristic recurrent neural circuitry model is developed to simulate the intricate “sleep–wake cycle autoregulation” process, in which the concomitance of STP and LTP is posited as a key factor in enabling this neural homeostasis.