11,830 research outputs found
Building Reservoir Computing Hardware Using Low Energy-Barrier Magnetics
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.Comment: To be presented at International Conference on Neuromorphic Systems
202
Universal discrete-time reservoir computers with stochastic inputs and linear readouts using non-homogeneous state-affine systems
A new class of non-homogeneous state-affine systems is introduced for use in
reservoir computing. Sufficient conditions are identified that guarantee first,
that the associated reservoir computers with linear readouts are causal,
time-invariant, and satisfy the fading memory property and second, that a
subset of this class is universal in the category of fading memory filters with
stochastic almost surely uniformly bounded inputs. This means that any
discrete-time filter that satisfies the fading memory property with random
inputs of that type can be uniformly approximated by elements in the
non-homogeneous state-affine family.Comment: 41 page
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