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IEEE Symposium on FPGA's for Custom Computing Machines (FCCM '95)
Implementing a genetic algorithm on a parallel custom computing machine
Napa Valley, California
April 19-April 21
ISBN: 0-8186-7086-X
| ASCII Text | x | ||
| N. Sitkoff, M. Wazlowski, A. Smith, H. Silverman, "Implementing a genetic algorithm on a parallel custom computing machine," Field-Programmable Custom Computing Machines, Annual IEEE Symposium on, pp. 0180, IEEE Symposium on FPGA's for Custom Computing Machines (FCCM '95), 1995. | |||
| BibTex | x | ||
| @article{ 10.1109/FPGA.1995.477424, author = {N. Sitkoff and M. Wazlowski and A. Smith and H. Silverman}, title = {Implementing a genetic algorithm on a parallel custom computing machine}, journal ={Field-Programmable Custom Computing Machines, Annual IEEE Symposium on}, volume = {0}, year = {1995}, isbn = {0-8186-7086-X}, pages = {0180}, doi = {http://doi.ieeecomputersociety.org/10.1109/FPGA.1995.477424}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - Field-Programmable Custom Computing Machines, Annual IEEE Symposium on TI - Implementing a genetic algorithm on a parallel custom computing machine SN - 0-8186-7086-X SP EP A1 - N. Sitkoff, A1 - M. Wazlowski, A1 - A. Smith, A1 - H. Silverman, PY - 1995 KW - genetic algorithms; mathematics computing; parallel machines; parallel architectures; circuit CAD; reconfigurable architectures; genetic algorithm; parallel custom computing machine; nonlinear optimization; chip partitioning problem; computation times; workstations; Armstrong III architecture; large design partitioning; computation bottlenecks VL - 0 JA - Field-Programmable Custom Computing Machines, Annual IEEE Symposium on ER - | |||
Abstract: Genetic algorithms (GAs) are a currently popular method for nonlinear optimization that can be used to provide a solution for the chip partitioning problem. Unfortunately, GAs usually require prohibitively large computation times on current workstations. This paper demonstrates the utility of the Armstrong III architecture by addressing the computational problems associated with partitioning large designs using GAs. An example GA is presented for chip partitioning that runs on Armstrong III. GA computation bottlenecks are identified and hardware implementation strategies are discussed. Results are presented that show the Armstrong III architecture can be adapted to execute a GA in significantly less time than current workstations.
Index Terms:
genetic algorithms; mathematics computing; parallel machines; parallel architectures; circuit CAD; reconfigurable architectures; genetic algorithm; parallel custom computing machine; nonlinear optimization; chip partitioning problem; computation times; workstations; Armstrong III architecture; large design partitioning; computation bottlenecks
Citation:
N. Sitkoff, M. Wazlowski, A. Smith, H. Silverman, "Implementing a genetic algorithm on a parallel custom computing machine," fccm, pp.0180, IEEE Symposium on FPGA's for Custom Computing Machines (FCCM '95), 1995
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