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)

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 (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)

FireSim: FPGA-Accelerated Cycle-Exact Scale-Out System Simulation in the Public Cloud

1 code implementation • 45th ACM/IEEE International Symposium on Computer Architecture (ISCA 2018) 2018 • Sagar Karandikar, Howard Mao, Donggyu Kim, David Biancolin, Alon Amid, Dayeol Lee, Nathan Pemberton, Emmanuel Amaro, Colin Schmidt, Aditya Chopra, Qijing Huang, Kyle Kovacs, Borivoje Nikolic, Randy Katz, Jonathan Bachrach, Krste Asanovic

We present FireSim, an open-source simulation platform that enables cycle-exact microarchitectural simulation of large scale-out clusters by combining FPGA-accelerated simulation of silicon-proven RTL designs with a scalable, distributed network simulation.