Massively parallel probabilistic computing with sparse Ising machines

Segmenting Text Images With Massively Parallel Machines

Image segmentation, the partitioning of an image into meaningful parts, is a major concern of any computer vision system. The meaningful parts of a text image are lines of text, words and characters. In this paper, the segmentation of pages of text into lines of text and lines of text into characters on a parallel machine will be examined. Using a parallel machine for text image segmentation al...

Time Sharing Massively Parallel Machines

Massively Parallel Computing in Economics

This paper discusses issues related to parallel computing in Economics. It highlights new methodologies and resources that are available for solving and estimating economic models and emphasizes situations when they are useful and others where they are impractical. Two examples illustrate the different ways parallel methods can be employed to speed computation as well as their limitations.

Object-Oriented Programming for Massively Parallel Machines

Large, robust massively parallel programs that are understandable (and therefore maintain-able) are not yet a reality. Such programs require a programming methodology that minimizes the conceptual diierences between the program and the domain addressed by the program, encourages reusability, and still produces robust programs that are readily maintained and reasoned about. This paper proposes t...

Massively Parallel Mixed-Signal VLSI Kernel Machines

Recently it has been shown that a simple learning paradigm, the support vector machine (SVM), outperforms some of the most elaborately tuned expert systems and neural networks in object recognition tasks. In run-time, the SVM operates by computing a kernelbased distance between the object’s vector at the input and a set of support vectors selected from the training set, and weighting the result...