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Reconfiguring the Bioinformatics Computational Spectrum: Challenges and Opportunities of FPGA-Based Bioinformatics Acceleration Platforms.

, , , , , , , , , , , and . IEEE Des. Test, 31 (1): 62-73 (2014)

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GPU technology as a platform for accelerating local complexity analysis of protein sequences., , , and . EMBC, page 2684-2687. IEEE, (2013)Towards systolic hardware acceleration for local complexity analysis of massive genomic data., , and . ACM Great Lakes Symposium on VLSI, page 339-344. ACM, (2012)Opportunities from the use of FPGAs as platforms for bioinformatics algorithms., , , , , , , , , and 2 other author(s). BIBE, page 559-565. IEEE Computer Society, (2012)FPGA-based hardware acceleration for local complexity analysis of massive genomic data., , , and . Integr., 46 (3): 230-239 (2013)A reconfigurable MPSoC-based QAM modulation architecture., , , , and . VLSI-SoC, page 137-142. IEEE, (2010)Towards optimal CMOS lifetime via unified reliability modeling and multi-objective optimization., , and . ISCAS, page 1049-1052. IEEE, (2011)A high performance hardware architecture for portable, low-power retinal vessel segmentation., , , and . Integr., 47 (3): 377-386 (2014)GPU technology as a platform for accelerating physiological systems modeling based on Laguerre-Volterra networks., , , and . EMBC, page 3283-3286. IEEE, (2015)An MPSoC-Based QAM Modulation Architecture with Run-Time Load-Balancing., , , , and . EURASIP J. Embed. Syst., (2011)Reconfiguring the Bioinformatics Computational Spectrum: Challenges and Opportunities of FPGA-Based Bioinformatics Acceleration Platforms., , , , , , , , , and 2 other author(s). IEEE Des. Test, 31 (1): 62-73 (2014)