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Open Access Publications from the University of California

Beyond Moore neuromorphic chips: harnessing complexity in atomic switch networks for alternative computing

  • Author(s): Scharnhorst, Kelsey
  • Advisor(s): Gimzewski, James K
  • et al.

The invention of the internet began the age of information as well as exponentially increased the number of complex systems in our world. As the age of information comes to an end, so does the persevering trend known as Moore's Law. This means that the number of circuit elements on an integrated chip will no longer double every two years, nor will the processing speed of computers. Personal computers utilize the Von Neumann architecture which separates storage from processing. This separation causes information transfer lags as a computer processes information much faster than it can be fetched from information storage. Thus, to circumvent both the limitations on elemental packing, and areal density a movement into neuromorphic hardware has occurred. Neuromorphic chips seek to emulate brain-like processing of information through low-power, highly parallel, densely interconnected, and closely packed individual elements which have a non-intuitive entangled relationship. This work explored the potential of atomic switch networks (ASNs) for reservoir, natural, and unconventional computing, provides evidence for ASNs as complex adaptive systems operating in and around the edge of chaos, presents a new material for use in ASNs, and evaluates spoken digit recognition using reservoir computing. A second project herein explores the maturation of human pluripotent stem cell-derived cardiomyocytes for use in studying heart disease, which is the leading cause of death in the world. Maturation of these cells is significant to the field. Via a chemically defined differentiation regimen with a monolayer cell culture technique on top of a multi-electrode array for real-time measurements of electrophysiological properties, in vivo development was reproduced. Both systems described above required data science analysis of time-series multi-electrode array information.

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