Experimental Demonstration of Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses.

ABSTRACT: Spiking neural networks (SNN) are computational models inspired by the brain's ability to naturally encode and process information in the time domain. The added temporal dimension is believed to render them more computationally efficient than the conventional artificial neural networks, though their full computational capabilities are yet to be explored. Recently, in-memory computing architectures based on non-volatile memory crossbar arrays have shown great promise to implement parallel computations in artificial and spiking neural networks. In this work, we evaluate the feasibility to realize high-performance event-driven in-situ supervised learning systems using nanoscale and stochastic analog memory synapses. For the first time, the potential of analog memory synapses to generate preci

SUBMITTER: Nandakumar SR 

PROVIDER: S-EPMC7228943 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature