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Title: A Biologically Plausible Sparse Approximation Solver on Neuromorphic Hardware
Committee:
Dr. David Anderson, ECE, Chair , Advisor
Dr. Justin Romberg, ECE
Dr. Christopher Rozell, ECE
Dr. Mark Davenport, ECE
Dr. Andreas Andreou, Johns Hopkins
Abstract:
This work delivers a neuromorphic system that solves for the sparse approximation on hardware geared toward real-world embedded systems signal processing applications. We choose to explore the biologically-plausible Locally Competitive Algorithm (LCA), a neural network that solves the sparse approximation problem. We implement this algorithm on the brain-inspired IBM Neurosynaptic System, also known as the TrueNorth chip, a specialized hardware platform that has shown success in deploying neural networks for signal processing applications.
