MLComp: A Methodology for Machine Learning-based Performance Estimation and Adaptive Selection of Pareto-Optimal Compiler Optimization Sequences
Alessio Colucci1,a, Dávid Juhász2,a, Martin Mosbeck2,b, Alberto Marchisio1,b, Semeen Rehman2,c, Manfred Kreutzer3,a, Günther Nadbath3,b, Axel Jantsch2,d and Muhammad Shafique1,4
1Institute of Computer Engineering, Technische Universität Wien (TUWien), Vienna, Austria
aalessio.colucci@tuwien.ac.at
balberto.marchisio@tuwien.ac.at
2TU Wien, Christian Doppler Laboratory for Embedded Machine Learning, Vienna, Austria
adavid.juhasz@tuwien.ac.at
bmartin.mosbeck@tuwien.ac.at
csemeen.rehman@tuwien.ac.at
daxel.jantsch@tuwien.ac.at
3ABIX GmbH, Vienna, Austria
amkreutzer@a-bix.com
bgnadbath@a-bix.com
4Division of Engineering, New York University Abu Dhabi, UAEh
muhammad.shafique@nyu.edu
ABSTRACT
Embedded systems have proliferated in various consumer and industrial applications with the evolution of Cyber-Physical Systems and the Internet of Things. These systems are subjected to stringent constraints so that embedded software must be optimized for multiple objectives simultaneously, namely reduced energy consumption, execution time, and code size. Compilers offer optimization phases to improve these metrics. However, proper selection and ordering of them depends on multiple factors and typically requires expert knowledge. State-ofthe- art optimizers facilitate different platforms and applications case by case, and they are limited by optimizing one metric at a time, as well as requiring a time-consuming adaptation for different targets through dynamic profiling.
To address these problems, we propose the novel MLComp methodology, in which optimization phases are sequenced by a Reinforcement Learning-based policy. Training of the policy is supported by Machine Learning-based analytical models for quick performance estimation, thereby drastically reducing the time spent for dynamic profiling. In our framework, different Machine Learning models are automatically tested to choose the best-fitting one. The trained Performance Estimator model is leveraged to efficiently devise Reinforcement Learning-based multi-objective policies for creating quasioptimal phase sequences.
Compared to state-of-the-art estimation models, our Performance Estimator model achieves lower relative error (< 2%) with up to 50⇥ faster training time over multiple platforms and application domains. Our Phase Selection Policy improves execution time and energy consumption of a given code by up to 12% and 6%, respectively. The Performance Estimator and the Phase Selection Policy can be trained efficiently for any target platform and application domain.
