@inproceedings{573eaa2d3a434c2b9635fd077642648c,
title = "An EDA Framework for Large Scale Hybrid Neuromorphic Computing Systems",
abstract = "In implementations of neuromorphic computing systems (NCS), memristor and its crossbar topology have been widely used to realize fully connected neural networks. However, many neural networks utilized in real applications often have a sparse connectivity, which is hard to be efficiently mapped to a crossbar structure. Moreover, the scale of the neural networks is normally much larger than that can be offered by the latest integration technology of memristor crossbars. In this work, we propose AutoNCS - an EDA framework that can automate the NCS designs that combine memristor crossbars and discrete synapse modules. The connections of the neural networks are clustered to improve the utilization of the memristor elements in crossbar structures by taking into account the physical design cost of the NCS. Our results show that AutoNCS can substantially enhance the utilization efficiency of memristor crossbars while reducing the wirelength, area and delay of the physical designs of the NCS.",
keywords = "Algorithms, Design, Neuromorphic Computing Systems, Neural Networks, Spectral Clustering, Memristor Crossbar, Sparsity, PLACEMENT",
author = "Wei Wen and Chi-Ruo Wu and Xiaofang Hu and Beiye Liu and Tsung-Yi Ho and Xin Li and Yiran Chen",
note = "Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to
[email protected].; 52nd ACM/EDAC/IEEE Design Automation Conference (DAC) ; Conference date: 08-06-2015 Through 12-06-2015",
year = "2015",
doi = "10.1145/2744769.2744795",
language = "English",
series = "Design Automation Conference DAC",
publisher = "IEEE Computer Society",
booktitle = "2015 52ND ACM/EDAC/IEEE DESIGN AUTOMATION CONFERENCE (DAC)",
address = "United States",
}