Parametric reconfigurable designs with Machine Learning Optimizer

We investigate the use of meta-heuristics and machine learning to automate reconfigurable application parameter optimization. The traditional approach involves two steps: (a) analyzing the application in order to create models and tools for exploration of the parameter space, and (b) exploring the parameter space using such tools. The proposed approach, called the Machine Learning Optimizer (MLO), involves a Particle Swarm Optimization (PSO) algorithm with an underlying surrogate fitness function model based on Gaussian Process (GP) and Support Vector Machines (SVMs). We present a case study of a quadrature based financial application with varied precision. We evaluate our approach by comparing the amount of benchmark evaluations and bit-stream generations when using MLO and when using the traditional approach.