Mathematica: a system for doing mathematics by computer
Mathematica: a system for doing mathematics by computer
Redundant operator creation: a scheduling optimization technique
DAC '91 Proceedings of the 28th ACM/IEEE Design Automation Conference
Critical path minimization using retiming and algebraic speed-up
DAC '93 Proceedings of the 30th international Design Automation Conference
Optimizing resource utilization and testability using hot potato techniques
DAC '94 Proceedings of the 31st annual Design Automation Conference
Compiler transformations for high-performance computing
ACM Computing Surveys (CSUR)
OSCAR: optimum simultaneous scheduling, allocation and resource binding based on integer programming
EURO-DAC '94 Proceedings of the conference on European design automation
VLSI and Modern Signal Processing
VLSI and Modern Signal Processing
EDTC '96 Proceedings of the 1996 European conference on Design and Test
Matching system and component behaviour in MIMOLA synthesis tools
EURO-DAC '90 Proceedings of the conference on European design automation
Optimizing power using transformations
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
A uniform optimization technique for offset assignment problems
Proceedings of the 11th international symposium on System synthesis
Improved interconnect sharing by identity operation insertion
ICCAD '99 Proceedings of the 1999 IEEE/ACM international conference on Computer-aided design
Fundamenta Informaticae - Application of concurrency to system design
Processor Description Languages
Processor Description Languages
Formal verification of code motion techniques using data-flow-driven equivalence checking
ACM Transactions on Design Automation of Electronic Systems (TODAES) - Special section on verification challenges in the concurrent world
Fundamenta Informaticae - Application of Concurrency to System Design
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This paper presents a novel approach for algebraic optimization of data-flow graphs in the domain of computationally intensive applications. The presented approach is based upon the paradigm of simulated evolution which has been proven to be a powerful method for solving large non-linear optimization problems. We introduce a genetic algorithm with a new chromosomal representation of data-flow graphs that serves as a basis for preserving the correctness of algebraic transformations and allows an efficient implementation of the genetic operators. Furthermore, we introduce a new class of hardware-related transformation rules which for the first time allow to take existing component libraries into account. The efficiency of our method is demonstrated by encouraging experimental results for several standard benchmarks.