Understanding Automatic Differentiation Pitfalls

no code implementations • 12 May 2023 • Jan Hückelheim, Harshitha Menon, William Moses, Bruce Christianson, Paul Hovland, Laurent Hascoët

Automatic differentiation, also known as backpropagation, AD, autodiff, or algorithmic differentiation, is a popular technique for computing derivatives of computer programs accurately and efficiently.

ProTuner: Tuning Programs with Monte Carlo Tree Search

no code implementations • 27 May 2020 • Ameer Haj-Ali, Hasan Genc, Qijing Huang, William Moses, John Wawrzynek, Krste Asanović, Ion Stoica

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.

Scheduling

AutoPhase: Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement Learning

1 code implementation • 2 Mar 2020 • Qijing Huang, Ameer Haj-Ali, William Moses, John Xiang, Ion Stoica, Krste Asanovic, John Wawrzynek

We compare the performance of AutoPhase to state-of-the-art algorithms that address the phase-ordering problem.

reinforcement-learning Reinforcement Learning (RL)

AutoPhase: Compiler Phase-Ordering for High Level Synthesis with Deep Reinforcement Learning

1 code implementation • 15 Jan 2019 • Ameer Haj-Ali, Qijing Huang, William Moses, John Xiang, Ion Stoica, Krste Asanovic, John Wawrzynek

We implement a framework in the context of the LLVM compiler to optimize the ordering for HLS programs and compare the performance of deep reinforcement learning to state-of-the-art algorithms that address the phase-ordering problem.

reinforcement-learning Reinforcement Learning (RL)