Building Reservoir Computing Hardware Using Low Energy-Barrier Magnetics
This work addresses the problem of building energy-efficient hardware for spatio-temporal data processing in edge devices, representing an incremental advancement in hardware design.
The paper tackled the challenge of implementing hardware reservoir computers by proposing an analog stochastic neuron cell using low energy-barrier magnetic tunnel junctions and transistors, enabling compact and energy-efficient signal processors for edge devices.
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.