Symbolic polynomial maximization over convex sets and its application to memory requirement estimation
Memory requirement estimation is an important issue in the development of embedded systems, since memory directly influences performance, cost and power consumption. It is therefore crucial to have tools that automatically compute accurate estimates of the memory requirements of programs to better c...
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | JOUR |
| Materias: | |
| Acceso en línea: | http://hdl.handle.net/20.500.12110/paper_10638210_v17_n8_p983_Clauss |
| Aporte de: |
| id |
todo:paper_10638210_v17_n8_p983_Clauss |
|---|---|
| record_format |
dspace |
| spelling |
todo:paper_10638210_v17_n8_p983_Clauss2023-10-03T16:01:53Z Symbolic polynomial maximization over convex sets and its application to memory requirement estimation Clauss, P. Fernández, F.J. Garbervetsky, D. Verdoolaege, S. Bernstein expansion Convex polytopes Memory requirement Program optimization Static program analysis Bernstein expansion Convex polytopes Memory requirement Program optimization Static program analysis Amber Embedded systems Optimization Polynomials Set theory Topology Convex optimization Memory requirement estimation is an important issue in the development of embedded systems, since memory directly influences performance, cost and power consumption. It is therefore crucial to have tools that automatically compute accurate estimates of the memory requirements of programs to better control the development process and avoid some catastrophic execution exceptions. Many important memory issues can be expressed as the problem of maximizing a parametric polynomial defined over a parametric convex domain. Bernstein expansion is a technique that has been used to compute upper bounds on polynomials defined over intervals and parametric boxes. In this paper, we propose an extension of this theory to more general parametric convex domains and illustrate its applicability to the resolution of memory issues with several application examples. © 2006 IEEE. Fil:Fernández, F.J. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Garbervetsky, D. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_10638210_v17_n8_p983_Clauss |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
| collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
Bernstein expansion Convex polytopes Memory requirement Program optimization Static program analysis Bernstein expansion Convex polytopes Memory requirement Program optimization Static program analysis Amber Embedded systems Optimization Polynomials Set theory Topology Convex optimization |
| spellingShingle |
Bernstein expansion Convex polytopes Memory requirement Program optimization Static program analysis Bernstein expansion Convex polytopes Memory requirement Program optimization Static program analysis Amber Embedded systems Optimization Polynomials Set theory Topology Convex optimization Clauss, P. Fernández, F.J. Garbervetsky, D. Verdoolaege, S. Symbolic polynomial maximization over convex sets and its application to memory requirement estimation |
| topic_facet |
Bernstein expansion Convex polytopes Memory requirement Program optimization Static program analysis Bernstein expansion Convex polytopes Memory requirement Program optimization Static program analysis Amber Embedded systems Optimization Polynomials Set theory Topology Convex optimization |
| description |
Memory requirement estimation is an important issue in the development of embedded systems, since memory directly influences performance, cost and power consumption. It is therefore crucial to have tools that automatically compute accurate estimates of the memory requirements of programs to better control the development process and avoid some catastrophic execution exceptions. Many important memory issues can be expressed as the problem of maximizing a parametric polynomial defined over a parametric convex domain. Bernstein expansion is a technique that has been used to compute upper bounds on polynomials defined over intervals and parametric boxes. In this paper, we propose an extension of this theory to more general parametric convex domains and illustrate its applicability to the resolution of memory issues with several application examples. © 2006 IEEE. |
| format |
JOUR |
| author |
Clauss, P. Fernández, F.J. Garbervetsky, D. Verdoolaege, S. |
| author_facet |
Clauss, P. Fernández, F.J. Garbervetsky, D. Verdoolaege, S. |
| author_sort |
Clauss, P. |
| title |
Symbolic polynomial maximization over convex sets and its application to memory requirement estimation |
| title_short |
Symbolic polynomial maximization over convex sets and its application to memory requirement estimation |
| title_full |
Symbolic polynomial maximization over convex sets and its application to memory requirement estimation |
| title_fullStr |
Symbolic polynomial maximization over convex sets and its application to memory requirement estimation |
| title_full_unstemmed |
Symbolic polynomial maximization over convex sets and its application to memory requirement estimation |
| title_sort |
symbolic polynomial maximization over convex sets and its application to memory requirement estimation |
| url |
http://hdl.handle.net/20.500.12110/paper_10638210_v17_n8_p983_Clauss |
| work_keys_str_mv |
AT claussp symbolicpolynomialmaximizationoverconvexsetsanditsapplicationtomemoryrequirementestimation AT fernandezfj symbolicpolynomialmaximizationoverconvexsetsanditsapplicationtomemoryrequirementestimation AT garbervetskyd symbolicpolynomialmaximizationoverconvexsetsanditsapplicationtomemoryrequirementestimation AT verdoolaeges symbolicpolynomialmaximizationoverconvexsetsanditsapplicationtomemoryrequirementestimation |
| _version_ |
1782024176256679936 |