Proceedings of the third international conference on Genetic algorithms
ADSP-2100 Family user's manual
ADSP-2100 Family user's manual
Optimizing stack frame accesses for processors with restricted addressing modes
Software—Practice & Experience
Storage assignment to decrease code size
ACM Transactions on Programming Languages and Systems (TOPLAS)
Algorithms for address assignment in DSP code generation
Proceedings of the 1996 IEEE/ACM international conference on Computer-aided design
A uniform optimization technique for offset assignment problems
Proceedings of the 11th international symposium on System synthesis
Address code generation for digital signal processors
Proceedings of the 38th annual Design Automation Conference
Code Optimization Techniques for Embedded Processors: Methods, Algorithms, and Tools
Code Optimization Techniques for Embedded Processors: Methods, Algorithms, and Tools
Storage assignment optimizations through variable coalescence for embedded processors
Proceedings of the 2003 ACM SIGPLAN conference on Language, compiler, and tool for embedded systems
Offset assignment showdown: evaluation of DSP address code optimization algorithms
CC'03 Proceedings of the 12th international conference on Compiler construction
Systematic integration of parameterized local search into evolutionary algorithms
IEEE Transactions on Evolutionary Computation
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Offset assignment has been studied as a highly effective approach to code optimization in modern digital signal processors (DSPs). In this paper, we propose two evolutionary algorithms to solve the general offset assignment problem with k address registers and an arbitrary auto-modify range. These algorithms differ from previous algorithms by having the capability of visiting the entire search space. We implement and analyze a variety of existing general offset assignment algorithms and test them on a set of standard benchmarks. The algorithms we propose can achieve a performance improvement of up to 31% over the best existing algorithm. We also achieve an average of 14% improvement over the union of recently proposed algorithms.