Exploring the opportunity of implementing neuromorphic computing systems with spintronic devices. Yan, B., <a href="https://homes.luddy.indiana.edu/fc7/" target="_blank">Chen, Fan</a></span>, Zhang, Y., Song, C., Li, H., & Chen, Y. In 2018 Design, Automation & Test in Europe Conference & Exhibition, DATE 2018, Dresden, Germany, March 19-23, 2018, pages 109–112, 2018. IEEE.
Exploring the opportunity of implementing neuromorphic computing systems with spintronic devices [link]Paper  doi  abstract   bibtex   
Many cognitive algorithms such as neural networks cannot be efficiently executed by von Neumann architectures, the performance of which is constrained by the memory wall between microprocessor and memory hierarchy. Hence, researchers started to investigate new computing paradigms such as neuromorphic computing that can adapt their structure to the topology of the algorithms and accelerate their executions. New computing units have been also invented to support this effort by leveraging emerging nano-devices. In this work, we will discuss the opportunity of implementing neuromorphic computing systems with spintronic devices. We will also provide insights on how spintronic devices fit into different part of neuromorphic computing systems. Approaches to optimize the circuits are also discussed.
@INPROCEEDINGS{DATE18dw, 
  author    = {Bonan Yan and
               {<a href="https://homes.luddy.indiana.edu/fc7/" target="_blank">Chen, Fan</a></span>} and
               Yaojun Zhang and
               Chang Song and
               Hai Li and
               Yiran Chen},
  title     = {Exploring the opportunity of implementing neuromorphic computing systems
               with spintronic devices},
  booktitle = {2018 Design, Automation {\&} Test in Europe Conference {\&}
               Exhibition, {DATE} 2018, Dresden, Germany, March 19-23, 2018},
  pages     = {109--112},
  publisher = {{IEEE}},
  year      = {2018},
  url       = {https://doi.org/10.23919/DATE.2018.8341988},
  doi       = {10.23919/DATE.2018.8341988},
  abstract  = {Many cognitive algorithms such as neural networks cannot be efficiently executed by von Neumann architectures, the performance of which is constrained by the memory wall between microprocessor and memory hierarchy. Hence, researchers started to investigate new computing paradigms such as neuromorphic computing that can adapt their structure to the topology of the algorithms and accelerate their executions. New computing units have been also invented to support this effort by leveraging emerging nano-devices. In this work, we will discuss the opportunity of implementing neuromorphic computing systems with spintronic devices. We will also provide insights on how spintronic devices fit into different part of neuromorphic computing systems. Approaches to optimize the circuits are also discussed.},
}
Downloads: 0