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Reconfigurable computing: architectures and design methods
Reconfigurable computing is becoming increasingly attractive for many applications. This survey covers two aspects of reconfigurable computing: architectures and design methods. The paper includesExpand
Accuracy-Guaranteed Bit-Width Optimization
An automated static approach for optimizing bit widths of fixed-point feedforward designs with guaranteed accuracy, called MiniBit, is presented. Expand
Wordlength optimization for linear digital signal processing
This paper presents an approach to the wordlength allocation and optimization problem for linear digital signal processing systems implemented as custom parallel processing units. Expand
A Reconfigurable Computing Approach for Efficient and Scalable Parallel Graph Exploration
In many application domains, data are represented using large graphs involving millions of vertices and billions of edges. Expand
Customizable elliptic curve cryptosystems
This paper presents a method for producing hardware designs for elliptic curve cryptography (ECC) systems over the finite field GF(2/sup m/), using the optimal normal basis for the representation of numbers. Expand
A hardware Gaussian noise generator using the Box-Muller method and its error analysis
We present a hardware Gaussian noise generator based on the Box-Muller method that provides highly accurate noise samples that are analytically guaranteed to be accurate to one unit in the last place. Expand
A comparison of CPUs, GPUs, FPGAs, and massively parallel processor arrays for random number generation
The future of high-performance computing is likely to rely on the ability to efficiently exploit huge amounts of parallelism. Expand
Comparing Three Heuristic Search Methods for Functional Partitioning in Hardware–Software Codesign
This paper compares three heuristic search algorithms: genetic algorithm (GA), simulated annealing (SA) and tabu search (TS), for hardware–software partitioning. Expand
Harnessing Human Computation Cycles for the FPGA Placement Problem
Harnessing human computation is an approach to find problem solutions. Expand
Gaussian random number generators
This article describes the algorithms underlying various GRNGs, compares their computational requirements and examines the quality of the random numbers with emphasis on the behaviour in the tail region of the Gaussian probability density function. Expand