학술논문

Symbolic Polynomial Maximization Over Convex Sets and Its Application to Memory Requirement Estimation
Document Type
Periodical
Source
IEEE Transactions on Very Large Scale Integration (VLSI) Systems IEEE Trans. VLSI Syst. Very Large Scale Integration (VLSI) Systems, IEEE Transactions on. 17(8):983-996 Aug, 2009
Subject
Components, Circuits, Devices and Systems
Computing and Processing
Polynomials
Upper bound
Structural engineering
Computer science
Embedded system
Costs
Energy consumption
Automatic control
Process control
Bernstein expansion
convex polytopes
memory requirement
program optimization
static program analysis
Language
ISSN
1063-8210
1557-9999
Abstract
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.