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Sparse matrix by vector multiplication (SMV) is a key operation of many scientific and engineering applications. Field programmable gate arrays (FPGAs) have the potential to significantly improve the performance of computationally intensive applications which are dominated by SMV. A shortcoming of most existing FPGA SMV implementations is that they use… (More)
Googlepsilas PageRank (PR) eigenvector problem is the worldpsilas largest matrix calculation. The algorithm is dominated by Sparse Matrix by Vector Multiplication (SMVM) where the matrix is very sparse, unsymmetrical and unstructured. The computation presents a serious challenge to general-purpose processors (GPP) and the result is a very lengthy… (More)
Network protection against link failure is required to allow packets to reach the destination with minimal packet loss when a failure occurs. When a link fails, traffic that attempts to use the failed link is interrupted. Typically, routers in the network discover the failure and find a new route to bypass the failed link. Alternatively, well-known segment… (More)
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Implementation of carrier-sensing-based medium access control (MAC) protocols on inexpensive reconfigurable radio platforms has proven challenging due to long and unpredictable delays associated with both signal processing on a general purpose processor (GPP) and the interface between the RF front-end and the GPP. This paper describes the development and… (More)
A hardware solver, implemented on an FPGA, for calculating the Page Rank eigenvector using the Power method is described. The critical operation is a very large sparse matrix by vector multiplication (SMVM). However, we show that the SMVM can be replaced with a pattern adder (a term coined to denote SMVM without multiplication), and a dense element by… (More)