Datapath synthesis using a problem-space genetic algorithm

This paper presents a new approach to datapath synthesis based on a problem-space genetic algorithm (PSGA). The proposed technique performs concurrent scheduling and allocation of functional units, registers, and multiplexers with the objective of finding both a schedule and an allocation which minimizes the cost function of the hardware resources and the total time of execution. The problem-space genetic algorithm based datapath synthesis system (PSGA-Synth) combines a standard genetic algorithm with a known heuristic to search the large design space in an intelligent manner. PSGA-Synth handles multicycle functional units, structural pipelining, conditional code and loops, and provides a mechanism to specify lower and upper bounds on the number of control steps. The PSGA-Synth was tested on a set of problems selected from the literature, as well as larger problems created by us, with promising results. PSGA-Synth not only finds the best known results for all the test problems examined in a relatively small amount of CPU time, but also has the ability to efficiently handle large problems. >

[1]  Yu-Chin Hsu,et al.  A formal approach to the scheduling problem in high level synthesis , 1991, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[2]  R. Storer,et al.  New search spaces for sequencing problems with application to job shop scheduling , 1992 .

[3]  David S. Johnson,et al.  Computers and Inrracrobiliry: A Guide ro the Theory of NP-Completeness , 1979 .

[4]  Alice C. Parker,et al.  Tutorial on high-level synthesis , 1988, DAC '88.

[5]  Srinivas Devadas,et al.  Algorithms for hardware allocation in data path synthesis , 1989, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[6]  Tai A. Ly,et al.  Applying simulated evolution to high level synthesis , 1993, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[7]  Pinaki Mazumder,et al.  A genetic approach to standard cell placement using meta-genetic parameter optimization , 1990, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[8]  Pierre G. Paulin,et al.  Force-directed scheduling for the behavioral synthesis of ASICs , 1989, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[9]  L. Darrell Whitley,et al.  Scheduling Problems and Traveling Salesmen: The Genetic Edge Recombination Operator , 1989, International Conference on Genetic Algorithms.

[10]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[11]  Joos Vandewalle,et al.  Background Memory Synthesis for Algebraic Algorithms on Multi-Processor DSP Chips , 1989 .

[12]  David S. Johnson,et al.  Computers and In stractability: A Guide to the Theory of NP-Completeness. W. H Freeman, San Fran , 1979 .

[13]  Peter Marwedel,et al.  Integrated Scheduling and Binding : A Synthesis Approach for Design Space Exploration , 1989, 26th ACM/IEEE Design Automation Conference.

[14]  John A. Nestor,et al.  Data path allocation using an extended binding model , 1992, [1992] Proceedings 29th ACM/IEEE Design Automation Conference.

[15]  Lawrence Davis,et al.  Job Shop Scheduling with Genetic Algorithms , 1985, ICGA.

[16]  Catherine H. Gebotys,et al.  Optimal synthesis of high-performance architectures , 1992 .

[17]  Donald E. Thomas,et al.  The combination of scheduling, allocation, and mapping in a single algorithm , 1991, DAC '90.

[18]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[19]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[20]  David E. Goldberg,et al.  Alleles, loci and the traveling salesman problem , 1985 .

[21]  Akihiro Hashimoto,et al.  Wire routing by optimizing channel assignment within large apertures , 1971, DAC.