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Systematic Comparison Of Reservoir Computing Frameworks, Nihar S. Koppolu, Christof Teuscher
Systematic Comparison Of Reservoir Computing Frameworks, Nihar S. Koppolu, Christof Teuscher
Student Research Symposium
In this poster, we present a systematic evaluation and comparison of five Reservoir computing (RC) software simulation frameworks, namely reservoirpy, RcTorch, pyRCN, pytorch-esn, and ReservoirComputing.jl. RC is a specific machine learning approach that leverages fixed, nonlinear systems to map signals into higher dimensions. Its unique strength lies in training only the readout layer, which reduces the training complexity. RC excels in temporal signal processing and is also well suited for various physical implementations. The increasing interest in RC has led to the proliferation of various RC simulation frameworks. Our RC simulation framework evaluation focuses on a feature comparison, documentation quality, …
Energy-Efficient Neuromorphic Architectures For Nuclear Radiation Detection Applications, Jorge I. Canales-Verdial, Jamison R. Wagner, Landon A. Schmucker, Mark Wetzel, Nathan J. Withers, Philippe Erol Proctor, Christof Teuscher, Multiple Additional Authors
Energy-Efficient Neuromorphic Architectures For Nuclear Radiation Detection Applications, Jorge I. Canales-Verdial, Jamison R. Wagner, Landon A. Schmucker, Mark Wetzel, Nathan J. Withers, Philippe Erol Proctor, Christof Teuscher, Multiple Additional Authors
Electrical and Computer Engineering Faculty Publications and Presentations
A comprehensive analysis and simulation of two memristor-based neuromorphic architectures for nuclear radiation detection is presented. Both scalable architectures retrofit a locally competitive algorithm to solve overcomplete sparse approximation problems by harnessing memristor crossbar execution of vector–matrix multiplications. The proposed systems demonstrate excellent accuracy and throughput while consuming minimal energy for radionuclide detection. To ensure that the simulation results of our proposed hardware are realistic, the memristor parameters are chosen from our own fabricated memristor devices. Based on these results, we conclude that memristor-based computing is the preeminent technology for a radiation detection platform.