Cory E. Merkel

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In this work, we propose a current-mode CMOS/memristor hybrid implementation of an extreme learning machine (ELM) architecture. We present novel circuit designs for linear, sigmoid,and threshold neuronal activation functions, as well as memristor-based bipolar synaptic weighting. In addition, this work proposes a stochastic version of the least-mean-squares(More)
As the efficiency of neuromorphic systems improves, biologically-inspired learning techniques are becoming more and more appealing for various computing applications, ranging from pattern and character recognition to general purpose reconfigurable logic. Due to their functional similarities to synapses in the brain, memristors are becoming a key element in(More)
Reservoir computing (RC) is gaining traction in several signal processing domains, owing to its non-linear stateful computation, spatiotemporal encoding, and reduced training complexity over recurrent neural networks (RNNs). Previous studies have shown the effectiveness of software-based RCs for a wide spectrum of applications. A parallel body of work(More)