Ansor: Generating High-Performance Tensor Programs for Deep Learning

no code implementations • 11 Jun 2020 • Lianmin Zheng, Chengfan Jia, Minmin Sun, Zhao Wu, Cody Hao Yu, Ameer Haj-Ali, Yida Wang, Jun Yang, Danyang Zhuo, Koushik Sen, Joseph E. Gonzalez, Ion Stoica

Ansor can find high-performance programs that are outside the search space of existing state-of-the-art approaches.

Vocal Bursts Intensity Prediction

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 +1

AutoCkt: Deep Reinforcement Learning of Analog Circuit Designs

1 code implementation • 6 Jan 2020 • Keertana Settaluri, Ameer Haj-Ali, Qijing Huang, Kourosh Hakhamaneshi, Borivoje Nikolic

Domain specialization under energy constraints in deeply-scaled CMOS has been driving the need for agile development of Systems on a Chip (SoCs).

Signal Processing

Gemmini: Enabling Systematic Deep-Learning Architecture Evaluation via Full-Stack Integration

5 code implementations • 22 Nov 2019 • Hasan Genc, Seah Kim, Alon Amid, Ameer Haj-Ali, Vighnesh Iyer, Pranav Prakash, Jerry Zhao, Daniel Grubb, Harrison Liew, Howard Mao, Albert Ou, Colin Schmidt, Samuel Steffl, John Wright, Ion Stoica, Jonathan Ragan-Kelley, Krste Asanovic, Borivoje Nikolic, Yakun Sophia Shao

DNN accelerators are often developed and evaluated in isolation without considering the cross-stack, system-level effects in real-world environments.

NeuroVectorizer: End-to-End Vectorization with Deep Reinforcement Learning

1 code implementation • 20 Sep 2019 • Ameer Haj-Ali, Nesreen K. Ahmed, Ted Willke, Sophia Shao, Krste Asanovic, Ion Stoica

However, these models are unable to capture the data dependency, the computation graph, or the organization of instructions.

Distributed, Parallel, and Cluster Computing Performance Programming Languages

A View on Deep Reinforcement Learning in System Optimization

no code implementations • 4 Aug 2019 • Ameer Haj-Ali, Nesreen K. Ahmed, Ted Willke, Joseph Gonzalez, Krste Asanovic, Ion Stoica

We propose a set of essential metrics to guide future works in evaluating the efficacy of using deep reinforcement learning in system optimization.

reinforcement-learning Reinforcement Learning +1

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 +1