Optimization of Data-Flow Computations Using Canonical TED Representation

Canonical representation for approximating solution of fuzzy polynomial equations

In this paper, the concept of canonical representation is proposed to find fuzzy roots of fuzzy polynomial equations. We transform fuzzy polynomial equations to system of crisp polynomial equations, this transformation is perform by using canonical representation based on three parameters Value, Ambiguity and Fuzziness. 

High-level Transformations of Data Flow Computations

This article describes a systematic method and an experimental software system to perform high-level transformation of the functional design specifications prior to high level synthesis. The initial specification is first transformed into a canonical form and then converted into a data flow graph (DFG) optimized for a particular application. The optimizing transformations are based on a canonic...

Runtime Data Flow Scheduling of Matrix Computations

We investigate the scheduling of matrix computations expressed as directed acyclic graphs for shared-memory parallelism. Because of the data granularity in this problem domain, even slight variations in load balance or data locality can greatly affect performance. Well-known scheduling algorithms such as work stealing have proven time and space bounds, but these bounds do not provide a discerna...

Canonical computations of cerebral cortex.

The idea that there is a fundamental cortical circuit that performs canonical computations remains compelling though far from proven. Here we review evidence for two canonical operations within sensory cortical areas: a feedforward computation of selectivity; and a recurrent computation of gain in which, given sufficiently strong external input, perhaps from multiple sources, intracortical inpu...

Data, Metadata, and Ted