Neuromorphic Computing

Prof. Hai “Helen” Li of Duke University, USA, works as a Hans Fischer Fellow in the Focus Group Neuromorphic Computing hosted by Prof. Ulf Schlichtmann (TUM Chair of Electronic Design Automation). As big data processing becomes pervasive and ubiquitous in our lives, the desire for embedded-everywhere and human-centric information systems calls for an intelligent computing paradigm that is capable of handling large volume of data through massively parallel operations under limited hardware and power resources. This demand, however, is unlikely to be satisfied through the traditional computer systems whose performance is greatly hindered by the increasing performance gap between CPU and memory as well as the fast-growing power consumption. Although we have not yet fully understood the working mechanism of human brains, the part that we have learned in past seventy years already guided us to many remarkable successes in computing applications, e.g., artificial neural network and machine learning. Our group works on “neuromorphic computing”, which stands for hardware acceleration of brain-inspired computing. The objective of our research is to innovate novel computing framework with ultra-high power efficiency by adopting the bio-inspired computation model and the advanced memristor technology. Creative applications of critical importance to nowadays mobile and embedded systems, including pattern recognition and video and image processing, will also be explored.

TUM-IAS funded doctoral candidate

Shuhang Zhang, Chair of Electronic Design Automation

Publications by the Focus Group

2019

  • Li, Bing; Yan, Bonan; Liu, Chenchen; Li, Hai (Helen): Build reliable and efficient neuromorphic design with memristor technology. Proceedings of the 24th Asia and South Pacific Design Automation Conference on - ASPDAC '19, ACM Press, 2019 mehr…

2018

  • Li, Bing; Chen, Fan; Kang, Wang; Zhao, Weisheng; Chen, Yiran; Li, Hai: Design and Data Management for Magnetic Racetrack Memory. 2018 IEEE International Symposium on Circuits and Systems (ISCAS), IEEE, 2018 mehr…
  • Li, Bing; Song, Linghao; Chen, Fan; Qian, Xuehai; Chen, Yiran; Li, Hai Helen: ReRAM-based accelerator for deep learning. 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE), IEEE, 2018 mehr…
  • Li, Bing; Wen, Wei; Mao, Jiachen; Li, Sicheng; Chen, Yiran; Li, Hai Helen: Running sparse and low-precision neural network: When algorithm meets hardware. 2018 23rd Asia and South Pacific Design Automation Conference (ASP-DAC), IEEE, 2018 mehr…
  • Song, Linghao; Zhuo, Youwei; Qian, Xuehai; Li, Hai; Chen, Yiran: GraphR: Accelerating Graph Processing Using ReRAM. 2018 IEEE International Symposium on High Performance Computer Architecture (HPCA), IEEE, 2018 mehr…
  • Yang, Qing; Li, Hai; Wu, Qing: A Quantized Training Method to Enhance Accuracy of ReRAM-based Neuromorphic Systems. 2018 IEEE International Symposium on Circuits and Systems (ISCAS), IEEE, 2018 mehr…

2017

  • Liu, Chenchen; Liu, Fuqiang; Li, Hai (Helen): Brain-inspired computing accelerated by memristor technology. Proceedings of the 4th ACM International Conference on Nanoscale Computing and Communication - NanoCom '17, ACM Press, 2017 mehr…
  • Song, Chang; Liu, Beiye; Wen, Wei; Li, Hai; Chen, Yiran: A quantization-aware regularized learning method in multilevel memristor-based neuromorphic computing system. 2017 IEEE 6th Non-Volatile Memory Systems and Applications Symposium (NVMSA), IEEE, 2017 mehr…
  • Yan, Bonan; Liu, Chenchen; Liu, Xiaoxiao; Chen, Yiran; Li, Hai: Understanding the trade-offs of device, circuit and application in ReRAM-based neuromorphic computing systems. 2017 IEEE International Electron Devices Meeting (IEDM), IEEE, 2017 mehr…
  • Yan, Bonan; Yang, Jianhua; Wu, Qing; Chen, Yiran; Li, Hai: A closed-loop design to enhance weight stability of memristor based neural network chips. 2017 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), IEEE, 2017 mehr…