Learn More
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 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)
We show that, in tantalum oxide resistive memories, activation power provides a multi-level variable for information storage that can be set and read separately from the resistance. These two state variables (resistance and activation power) can be precisely controlled in two steps: (1) the possible activation power states are selected by partially reducing(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.
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)
Experience in: analytical modeling, computer simulation, data mining techniques, and extensive experimental. • Detailed analytical modeling of electronic devices. • Development of computer memory devices for radiation-hard and cyber-attack resilient applications. • Design of hardware-based neuromorphic systems. • Technology development and business planning(More)