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.
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Kulkarni, S.R., Rajendran, B. (2015). Scalable Digital CMOS Architecture for Spike Based Supervised Learning. In: Iliadis, L., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2015. Communications in Computer and Information Science, vol 517. Springer, Cham. https://doi.org/10.1007/978-3-319-23983-5_15
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DOI: https://doi.org/10.1007/978-3-319-23983-5_15
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