Symbolic polynomial maximization over convex sets and its application to memory requirement estimation
Clauss, Philippe × Fernandez, Federico Javier Garbervetsky, Diego Verdoolaege, Sven #
Institute of Electrical and Electronics Engineers
IEEE Transactions on Very Large Scale Integration (VLSI) Systems vol:17 issue:8 pages:983-996
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.
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.
for a related presentation.