Abstract-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. The optimization of heterogeneous computing applications often requires substantial effort from the designer who has to analyze the application, create models and benchmarks and subsequently use them to optimize the application. One could try to employ exhaustive search of the application parameter space to carry out optimization yet it is unrealistic since benchmark evaluations involve bit-stream generation and code execution which takes hours of computing time. Recently it has been shown useful to use surrogate models combined with fitness functions for computationally expensive optimization problems in various fields [1], [2], [3], [4], [5] . As these models are orders of magnitude cheaper, they can substantially decrease optimization cost thus allowing for an automated approach. This is the motivation behind MLO which we apply to non-linear and multi-modal problem of heterogeneous application parameter optimization. We use GPs to model performance of the design like execution time or throughput, while searching for the global optimum using PSO. We classify the parameter space using SVMs to identify designs that would fail constraints; over-map on resources, produce inaccurate results or other. Our contributions: • The proposed approach has two new aspects: (a) surrogate models of application benchmarks (b) automated application optimization scheme based on the surrogate models and MLO (Section II). • We use our approach to optimize throughput of a quadrature based financial application with varied precision [6] . We show how MLO can significantly decrease benchmark evaluations (up to 85%) and can be used in combination with analytical models (Section III).
In REF the authors investigate the use of advanced meta-heuristic techniques along with machine learning to automate the optimization of a reconfigurable application parameter set.
Parametric reconfigurable designs with Machine Learning Optimizer
2012 International Conference on Field-Programmable Technology
2012 International Conference on Field-Programmable Technology
Computer Science