Memristor Crossbar-Based Neuromorphic Computing System: A Case Study

By Miao Hu, Hai Helen Li, Yiran Chen, Qing Wu, G. Rose, R. Linderman. IEEE Transactions on Neural Networks and Learning Systems, 2014

350 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/b518206a4ff92146302a1bf33814a411aff11a36

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

1. Miao Hu
2. John Paul Strachan

In Cognitive Neuroscience

1. John Paul Strachan

In Electrical and Electronic Engineering

1. Miao Hu
2. Ning Ge
3. John Paul Strachan
4. Shimeng Yu

Citations in the corpus

• Dot-product engine for neuromorphic computing: Programming 1T1M crossbar to accelerate matrix-vector multiplication (2016, background): “Some researchers use hardware training schemes to minimize errors [6, 7, 8, 9].”
• A Survey of Neuromorphic Computing and Neural Networks in Hardware (2017, neutral): “[1022], [1157], [1158], [1172], [1304], [1312], [1316], [1317],”