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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.
Machine learning electron correlation in a disordered medium
Learning from data has led to a paradigm shift in computational materials science.
Reservoir Computing based Neural Image Filters
Clean images are an important requirement for machine vision systems to recognize visual features correctly.
Hardware based Spatio-Temporal Neural Processing Backend for Imaging Sensors: Towards a Smart Camera
In this work we show how we can build a technology platform for cognitive imaging sensors using recent advances in recurrent neural network architectures and training methods inspired from biology.
Reservoir Computing using Stochastic p-Bits
We present a general hardware framework for building networks that directly implement Reservoir Computing, a popular software method for implementing and training Recurrent Neural Networks and are particularly suited for temporal inferencing and pattern recognition.
