Memristive Reservoir Computing Architecture for Epileptic Seizure Detection | Zendy

Cory Merkel | Zendy; Qutaiba M. Saleh | Zendy; Colin Donahue | Zendy; Dhireesha Kudithipudi | Zendy

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Memristive Reservoir Computing Architecture for Epileptic Seizure Detection

Author(s) -

Cory Merkel,

Qutaiba M. Saleh,

Colin Donahue,

Dhireesha Kudithipudi

Publication year - 2014

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2014.11.110

Subject(s) - computer science , reservoir computing , scalability , echo state network , network topology , software , artificial neural network , enhanced data rates for gsm evolution , recurrent neural network , computer hardware , distributed computing , computer architecture , artificial intelligence , computer network , operating system

Echo state networks (ESN) or reservoirs, are random, recurrent neural network topologies that integrate temporal data over short time windows by operating on the edge of chaos. Recently, there is a significant effort on the mathematical modeling and software topologies of the reservoirs. However, hardware reservoir fabrics are essential to deploy in energy constrained environments. In this paper, we investigate a hardware reservoir with bi-stable memristive synapses. In particular, we demonstrate a scalable hardware model for detecting real-time epileptic seizures in human models. The performance of the proposed reservoir hardware is evaluated for absent seizure signals with 85% accuracy

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