Stochastic search for signal processing algorithm optimization
- 10 November 2001
- proceedings article
- Published by Association for Computing Machinery (ACM)
Abstract
This paper presents an evolutionary algorithm for searching for the optimal implementations of signal transforms and compares this approach against other search techniques. A single signal processing algorithm can be represented by a very large number of different but mathematically equivalent formulas. When these formulas are implemented in actual code, unfortunately their running times differ significantly. Signal processing algorithm optimization aims at finding the fastest formula. We present a new approach that successfully solves this problem, using an evolutionary stochastic search algorithm, STEER, to search through the very large space of formulas. We empirically compare STEER against other search methods, showing that it notably can find faster formulas while still only timing a very small portion of the search space.Keywords
This publication has 6 references indexed in Scilit:
- In search of the optimal Walsh-Hadamard transformPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Fast Automatic Generation of DSP AlgorithmsPublished by Springer Nature ,2001
- SPLPublished by Association for Computing Machinery (ACM) ,2001
- Optimizing matrix multiply using PHiPACPublished by Association for Computing Machinery (ACM) ,1997
- High-level optimization via automated statistical modelingPublished by Association for Computing Machinery (ACM) ,1995
- The design of optimal DFT algorithms using dynamic programmingIEEE Transactions on Acoustics, Speech, and Signal Processing, 1983