Building Reservoir Computing Hardware Using Low Energy-Barrier Magnetics

• URL: http://arxiv.org/abs/2007.02766v1
• Date: Mon, 6 Jul 2020 14:11:45 GMT
• Title: Building Reservoir Computing Hardware Using Low Energy-Barrier Magnetics
• Authors: Samiran Ganguly, Avik W. Ghosh
• Abstract summary: Biologically recurrent neural networks, such as reservoir computers are of interest from a hardware point of view due to the simple learning scheme and deep connections to Kalman filters. Compact implementation of reservoir computers using such devices may enable building compact, energy-efficient signal processors for standalone or insitu machine cognition in edge devices.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Biologically inspired recurrent neural networks, such as reservoir computers are of interest in designing spatio-temporal data processors from a hardware point of view due to the simple learning scheme and deep connections to Kalman filters. In this work we discuss using in-depth simulation studies a way to construct hardware reservoir computers using an analog stochastic neuron cell built from a low energy-barrier magnet based magnetic tunnel junction and a few transistors. This allows us to implement a physical embodiment of the mathematical model of reservoir computers. Compact implementation of reservoir computers using such devices may enable building compact, energy-efficient signal processors for standalone or in-situ machine cognition in edge devices.

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