University of Central Florida Research Foundation Inc

Carnegie Mellon University

Sumit Kumar Jha

Alvaro Velasquez

Joseph E Van Nostrand

Dilia E Rodriguez

Christopher J Langmead

An in-memory computing architecture is disclosed that can evaluate the transitive closure of graphs using the natural parallel flow of information in 3-D nanoscale crossbars. The architecture can be implemented using 3-D crossbar architectures with as few as two layers of 1-diode 1-resistor (1D1R) interconnects. The architecture avoids memory-processor bottlenecks and can hence scale to large graphs. The approach leads to a runtime complexity of O(n) using O(n) memristor devices. This compares favorably to conventional algorithms with a time complexity of O((n)/p+(n) log p) on p processors. The approach takes advantage of the dynamics of 3-D crossbars not available on 2-D crossbars.

3-D crossbar architecture for fast energy-efficient in-memory computing of graph transitive closure

A stochastic computational model parameter synthesis system comprising at least one processor capable of executing processor executable code, and a non-transitory computer memory operably coupled with the at least one processor and storing processor executable code, which when executed by processor, causes processor to synthesize at least one parameter of a stochastic computational model to satisfy one or more behavioral specifications of properties observed in a modeled system. The processor generates and searches randomized projections of a first parameter space having n dimensions into one or more second abstract parameter space having d dimensions, where d is less than n, and outputs a signal indicative of a synthesized parameter value to the user.

Stochastic computational model parameter synthesis system

Memristor-based nano-crossbar computing is a revolutionary computing paradigm that does away with the traditional Von Neumann architectural separation of memory and computation units. The computation of Boolean formulas using memristor circuits has been a subject of several recent investigations. Crossbar computing, in general, has also been a topic of active interest, but sneak paths have posed a hurdle in the design of pervasive general-purpose crossbar computing paradigms. Various embodiments are disclosed which demonstrate that sneak paths in nano-crossbar computing can be exploited to design a Boolean-formula evaluation strategy. Such nano-crossbar designs are also an effective approach for synthesizing high performance customized arithmetic and logic circuits.

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Oviedo, FL, United States of America

Sumit Kumar Jha