This paper presents a new methodology based on evolutionary multi-objective optimization (EMO) to synthesize multiple complex modules on programmable devices (FPGAs). It starts from a behavioral description written in a common high-level language (for instance C) to automatically produce the register-transfer level (RTL) design in a hardware description language (e.g. Verilog). Since all high-level synthesis problems (scheduling, allocation and binding) are notoriously NP-complete and interdependent, the three problems should be considered simultaneously. This drives to a wide design space, that needs to be thoroughly explored to obtain solutions able to satisfy the design constraints. Evolutionary algorithms are good candidates to tackle such complex explorations. In this paper we provide a solution based on the non-dominated sorting genetic algorithm (NSGA-II) to explore the design space in order obtain the best solutions in terms of performance given the area constraints of a target FPGA device. Moreover, it has been integrated a good cost estimation model to guarantee the quality of the solutions found without requiring a complete synthesis for the validation of each generation, an impractical and time consuming operation. We show on the JPEG case study that the proposed approach provides good results in terms of trade-off between total area occupied and execution time.

An Evolutionary Approach to Area-Time Optimization of FPGA designs

FERRANDI, FABRIZIO;LANZI, PIER LUCA;PALERMO, GIANLUCA;PILATO, CHRISTIAN;SCIUTO, DONATELLA;TUMEO, ANTONINO
2007

Abstract

This paper presents a new methodology based on evolutionary multi-objective optimization (EMO) to synthesize multiple complex modules on programmable devices (FPGAs). It starts from a behavioral description written in a common high-level language (for instance C) to automatically produce the register-transfer level (RTL) design in a hardware description language (e.g. Verilog). Since all high-level synthesis problems (scheduling, allocation and binding) are notoriously NP-complete and interdependent, the three problems should be considered simultaneously. This drives to a wide design space, that needs to be thoroughly explored to obtain solutions able to satisfy the design constraints. Evolutionary algorithms are good candidates to tackle such complex explorations. In this paper we provide a solution based on the non-dominated sorting genetic algorithm (NSGA-II) to explore the design space in order obtain the best solutions in terms of performance given the area constraints of a target FPGA device. Moreover, it has been integrated a good cost estimation model to guarantee the quality of the solutions found without requiring a complete synthesis for the validation of each generation, an impractical and time consuming operation. We show on the JPEG case study that the proposed approach provides good results in terms of trade-off between total area occupied and execution time.
Proceedings of IC-SAMOS 2007. International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation, 2007.
1424410584
File in questo prodotto:
File Dimensione Formato  
SAMOS07HLS.pdf

Accesso riservato

: Post-Print (DRAFT o Author’s Accepted Manuscript-AAM)
Dimensione 2.02 MB
Formato Adobe PDF
2.02 MB Adobe PDF   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/248119
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 15
  • ???jsp.display-item.citation.isi??? 12
social impact