@inproceedings{4852fbf363d042b392896689159d69e1,
title = "Scalable digital cmos architecture for spike based supervised learning",
abstract = "Supervised learning algorithm for Spiking Neural Networks (SNN) based on Remote Supervised Method (ReSuMe) uses spike timing dependent plasticity (STDP) to adjust the synaptic weights. In this work, we present an optimal network configuration amenable to digital CMOS implementation and show that just 5 bits of resolution for the synaptic weights is sufficient to achieve fast convergence. We estimate that the implementation of this optimal network architecture in 65 nm and a futuristic 10 nm digital CMOS could result in systems with close to 0. 85 and 30 Million Synaptic Updates Per Second (MSUPS)/Watt.",
keywords = "Bit-precision, Digital neuromorphic architecture, Spiking neural networks, Supervised learning",
author = "Kulkarni, {Shruti R.} and Bipin Rajendran",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 16th International Conference on Engineering Applications of Neural Networks, EANN 2015 ; Conference date: 25-09-2015 Through 28-09-2015",
year = "2015",
doi = "10.1007/978-3-319-23983-5_15",
language = "English (US)",
isbn = "9783319239811",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "149--158",
editor = "Lazaros Iliadis and Chrisina Jayne",
booktitle = "Engineering Applications of Neural Networks - 16th International Conference, EANN 2015, Proceedings",
address = "Germany",
}