Yang Jiang
5 Papers
Yang Jiang is an academic researcher. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 1, co-authored 4 publications.
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Papers
Spontaneous Threshold Lowering Neuron using Second-Order Diffusive Memristor for Self-Adaptive Spatial Attention.
Yang Jiang,Dingchen Wang,Shuhui Shi,Yi Zhang,Shaocong Wang,Xin Chen,Hegan Chen,Yinan Lin,Kam Chi Loong,Jia Chen,Yi-Da Li,Renrui Fang,Dashan Shang,Qing Wang,Hongyu Yu,Zhongrui Wang +15 more
TL;DR: In this article , a second-order memristor with intrinsic spontaneous threshold lowering (STL) was used to solve the memory bottleneck of the von Neumann architecture commonly used in conventional digital computers and is deemed a promising solution to this bioinspired computing paradigm.
25
Comprehensive review of gallium nitride (GaN)-based gas sensors and their dynamic responses
Yang Jiang,Wenmao Li,Fangzhou Du,Robert Sokolovskij,Yi Zhang,Shuhui Shi,Qing Wang,Hongyu Yu,Zhongrui Wang +8 more
TL;DR: In this paper , the authors proposed a real-time, dependable and low-concentration gas detection system for monitoring toxic and high concentration gases in the air. But, their system is not suitable for the real-world environment.
14
Structural plasticity‐based hydrogel optical Willshaw model for one‐shot on‐the‐fly edge learning
Dingchen Wang,Dingyao Liu,Yinan Lin,Anran Yuan,Woyu Zhang,Yaping Zhao,Shaocong Wang,Xin Chen,Hegan Chen,Yi Zhang,Yang Jiang,Shuhui Shi,Kam Chi Loong,Jia Chen,Songrui Wei,Qing Wang,Hongyu Yu,Renjing Xu,Dashan Shang,Han Zhang,Shiming Zhang,Zhongrui Wang +21 more
TL;DR: In this article , a hydrogel-based optical Willshaw model (HOWM) is proposed for one-shot on-the-fly edge learning, which can be used for pattern classification, association and denoising.
Convolutional Echo‐State Network with Random Memristors for Spatiotemporal Signal Classification
Shaocong Wang,Hegan Chen,Woyu Zhang,Yi Li,Dingchen Wang,Shuhui Shi,Yaping Zhao,Kam Chi Loong,Xin Chen,Yujiao Dong,Yi Zhang,Yang Jiang,C.M. Furqan,Jia Chen,Qing Wang,Xiaoxin Xu,Guangdi Wang,Hongyu Yu,Dashan Shang,Zhongrui Wang +19 more
TL;DR: Random convolutional echo‐state network (RCESN) is a promising solution for the future smart edge hardware that retains 187.79× and 93.66× improvement of energy efficiency compared to the digital alternatives on the representative Human Activity Recognition Using Smartphones (HAR) and CRICKET datasets, respectively.
Random resistive memory-based deep extreme point learning machine for unified visual processing
Shaocong Wang,Yizhao Gao,Yi Li,Woyu Zhang,Yifei Yu,Bo Fei Wang,Ning Lin,Hegan Chen,Yue Zhang,Yang Jiang,Dingchen Wang,Jia Chen,Peng Dai,Hao Jiang,Peng Lin,Xumeng Zhang,Xiaojuan Qi,Xiaoxin Xu,Hayden K.-H. So,Zhongrui Wang,Dashan Shang,Qi Liu,Kwang-Ting Cheng,Ming Liu +23 more
TL;DR: A novel hardware-software co-design, random resistive memory-based deep extreme point learning machine (DEPLM), that offers efficient unified point set analysis and may pave the way for energy-efficient and training-friendly edge AI across various data modalities and tasks.