Matthew J. Marinella

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The exponential increase in data over the last decade presents a significant challenge to analytics efforts that seek to process and interpret such data for various applications. Neural-inspired computing approaches are being developed in order to leverage the computational properties of the analog, low-power data processing observed in biological systems.(More)
The brain is capable of massively parallel information processing while consuming only ∼1-100 fJ per synaptic event. Inspired by the efficiency of the brain, CMOS-based neural architectures and memristors are being developed for pattern recognition and machine learning. However, the volatility, design complexity and high supply voltages for CMOS(More)
Neuromemristive systems (NMSs) are gaining traction as an alternative to conventional CMOS-based von Neumann systems because of their greater energy and area efficiency. A proposed NMS accelerator for machine-learning tasks reduced power dissipation by five orders of magnitude, relative to a multicore reduced-instruction set computing processor.
The steady-state solution of filamentary memristive switching may be derived directly from the heat equation, modelling vertical and radial heat flow. This solution is shown to provide a continuous and accurate description of the evolution of the filament radius, composition, heat flow, and temperature during switching, and is shown to apply to a large(More)
Resistive memories enable dramatic energy reductions for neural algorithms. We propose a general purpose neural architecture that can accelerate many different algorithms and determine the device properties that will be needed to run backpropagation on the neural architecture. To maintain high accuracy, the read noise standard deviation should be less than(More)
Nonvolatile redox transistors (NVRTs) based upon Li-ion battery materials are demonstrated as memory elements for neuromorphic computer architectures with multi-level analog states, "write" linearity, low-voltage switching, and low power dissipation. Simulations of backpropagation using the device properties reach ideal classification accuracy.(More)
The inverter is still considered the weakest link in modern photovoltaic systems. Inverter failure can be classified into three major categories: manufacturing and quality control problems, inadequate design, and electrical component failure. It is often difficult to deconvolve the latter two of these, as electrical components can fail due to inadequate(More)
The field of SiC electronics has progressed rapidly in recent years, but certain electronic properties remain poorly understood. For example, a consensus has not been reached as to the specific point defects which limit minority carrier recombination, and little is known about defects which limit generation lifetimes. This paper investigates generation(More)
Recovery transients following blocking-state voltage stress are analyzed for two types of AlGaN/GaN HEMTs, one set of devices with thick AlGaN barrier layers and another with recessed-gate geometry and ALD SiO2 gate dielectric. Results show temperature-invariant emission processes are present in both devices. Recessedgate devices with SiO2 dielectrics are(More)