Search Results for author: Benoit Steiner

Found 9 papers, 6 papers with code

CompilerGym: Robust, Performant Compiler Optimization Environments for AI Research

1 code implementation17 Sep 2021 Chris Cummins, Bram Wasti, Jiadong Guo, Brandon Cui, Jason Ansel, Sahir Gomez, Somya Jain, Jia Liu, Olivier Teytaud, Benoit Steiner, Yuandong Tian, Hugh Leather

What is needed is an easy, reusable experimental infrastructure for real world compiler optimization tasks that can serve as a common benchmark for comparing techniques, and as a platform to accelerate progress in the field.

OpenAI Gym

Using Graph Neural Networks to model the performance of Deep Neural Networks

no code implementations27 Aug 2021 Shikhar Singh, Benoit Steiner, James Hegarty, Hugh Leather

State-of-the-art deep-learning compilers like TVM and Halide incorporate a learning-based performance model to search the space of valid implementations of a given deep learning algorithm.

Learning Space Partitions for Path Planning

1 code implementation19 Jun 2021 Kevin Yang, Tianjun Zhang, Chris Cummins, Brandon Cui, Benoit Steiner, Linnan Wang, Joseph E. Gonzalez, Dan Klein, Yuandong Tian

Path planning, the problem of efficiently discovering high-reward trajectories, often requires optimizing a high-dimensional and multimodal reward function.

Value Function Based Performance Optimization of Deep Learning Workloads

no code implementations30 Nov 2020 Benoit Steiner, Chris Cummins, Horace He, Hugh Leather

As machine learning techniques become ubiquitous, the efficiency of neural network implementations is becoming correspondingly paramount.

A Hierarchical Model for Device Placement

no code implementations ICLR 2018 Azalia Mirhoseini, Anna Goldie, Hieu Pham, Benoit Steiner, Quoc V. Le, Jeff Dean

We introduce a hierarchical model for efficient placement of computational graphs onto hardware devices, especially in heterogeneous environments with a mixture of CPUs, GPUs, and other computational devices.

Machine Translation Translation

Device Placement Optimization with Reinforcement Learning

1 code implementation ICML 2017 Azalia Mirhoseini, Hieu Pham, Quoc V. Le, Benoit Steiner, Rasmus Larsen, Yuefeng Zhou, Naveen Kumar, Mohammad Norouzi, Samy Bengio, Jeff Dean

Key to our method is the use of a sequence-to-sequence model to predict which subsets of operations in a TensorFlow graph should run on which of the available devices.

Language Modelling Machine Translation +1

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