Title:
"Machine Learning Meets Signal Processing Algorithm Optimization"
Subtitle:
"Features? What are Features? Cooley-Tukey? Huh?"
Abstract:
This talk reports on our work and results framing signal processing
algorithm optimization as a machine learning task. A single signal
processing algorithm can be represented by many different but
mathematically equivalent formulas. When these formulas are implemented in
actual code, they have very different running times. Signal processing
optimization is concerned with finding a formula that implements the
algorithm as efficiently as possible. Unfortunately, a correct mapping
between a mathematical formula and its running time is unknown. However
empirical performance data can be gathered for a variety of formulas. This
data offers an interesting opportunity to learn to predict running
time performance. In this talk we present two major results along this
direction: (1) Different sets of features are identified for mathematical
formulas that distinguish them into partitions with significantly different
running times, and (2) A function approximator can learn to accurately
predict the running time of a formula given a limited set of training data.
Showing the impact of selecting different features to describe the input,
this work contributes an extensive study on the role of learning for this
novel task.