This paper investigates the compilation of a committed-choice rulebased language, Constraint Handling Rules (CHR), to specialized hardware circuits. The developed hardware is able to turn the intrinsic concurrency of the language into parallelism. Rules are applied by a custom executor that handles

Low latency complex event processing on parallel hardware

Parallel Accelerator), is described in detail, including architecture and special hardware for GA computations. Mapping studies are given for centralized and distributed GA models. Keywords: parallel GA, parallel implementation, parallel computer, tree shape architecture. 19.1 Introduction Genetic

of algorithmic efficiency, hardware requirement, processing speed and error performances. In this paper for the sake of providing security and surveillance, a concept to detect the motion of the objects in video frames as well as a suitable parallel hardware architecture for the implementation of this algorithm

, and programmable co-processors. The difficulty of comparing hardware from published performance figures is also noted. Introduction The vast databases produced by the Human Genome Project demand innovative tools for fast sequence and database analysis. Because sequence databases contain billions of characters

hardware architecture for real-time eye detection. We use the AdaBoost algorithm with modified census transform(MCT) to detect eyes on a face image. We parallelize part of the algorithm to speed up processing. Several downscaled pyramid images of the eye candidate region are generated in parallel using

reactor. In this paper, we present a fast algorithm for tomographic reconstruction. A good property of our approach is that it fits well to hardware with two levels of parallelism (e.g. a GPU cluster). We also propose an objective evaluation method for measuring the quality of reconstruction on real

Trends in increasing web traffic demand an increase in server throughput while preserving energy efficiency and total cost of ownership. Present work in optimizing data center effi-ciency primarily focuses on the data center as a whole, using off-the-shelf hardware for individual servers. Server

The increasing complexity of network models poses a growing computational burden. At the same time, computational neuroscientists are finding it easier to access parallel hardware, such as multiprocessor personal computers, workstation clusters, and massively parallel supercomputers

. Parallel hardware is commercially available today at all levels of granularity -- from transistors through to vector processing MPPs. Most of this is userprogrammable, also at most levels of parallel granularity. A serious problem is that in order to get economically worthwhile performance from