ANNETTE: Accurate Neural Network Execution Time Estimation with Stacked Models

1 code implementation • 7 May 2021 • Matthias Wess, Matvey Ivanov, Anvesh Nookala, Christoph Unger, Alexander Wendt, Axel Jantsch

To decouple the architectural search from the target hardware, we propose a time estimation framework that allows for modeling the inference latency of DNNs on hardware accelerators based on mapping and layer-wise estimation models.

MLComp: A Methodology for Machine Learning-based Performance Estimation and Adaptive Selection of Pareto-Optimal Compiler Optimization Sequences

no code implementations • 9 Dec 2020 • Alessio Colucci, Dávid Juhász, Martin Mosbeck, Alberto Marchisio, Semeen Rehman, Manfred Kreutzer, Guenther Nadbath, Axel Jantsch, Muhammad Shafique

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.

Compiler Optimization reinforcement-learning +1