7 citations · 14 across the 3 of their papers we have counts for
5 papers
ProTuner: Tuning Programs with Monte Carlo Tree Search
Ameer Haj-Ali, Hasan Genc, Qijing Huang +4
We explore applying the Monte Carlo Tree Search (MCTS) algorithm in a notoriously difficult task: tuning programs for high-performance deep learning and image processing. We build…
AutoPhase: Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement Learning
Qijing Huang, Ameer Haj-Ali, William Moses +4
The performance of the code a compiler generates depends on the order in which it applies the optimization passes. Choosing a good order--often referred to as the phase-ordering pr…
Extracting Incentives from Black-Box Decisions
Yonadav Shavit, William S. Moses
An algorithmic decision-maker incentivizes people to act in certain ways to receive better decisions. These incentives can dramatically influence subjects' behaviors and lives, and…
AutoPhase: Compiler Phase-Ordering for High Level Synthesis with Deep Reinforcement Learning
Ameer Haj-Ali, Qijing Huang, William Moses +4
The performance of the code generated by a compiler depends on the order in which the optimization passes are applied. In high-level synthesis, the quality of the generated circuit…
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
Nicolas Vasilache, Oleksandr Zinenko, Theodoros Theodoridis +6
Deep learning models with convolutional and recurrent networks are now ubiquitous and analyze massive amounts of audio, image, video, text and graph data, with applications in auto…