luciusmos / maestro

An analytical cost model evaluating DNN mappings (dataflows and tiling).

MAESTRO: An Open-source Infrastructure for Modeling Dataflows within Deep Learning Accelerators

What is MAESTRO?

MAESTRO is an open-source tool for modeling and evaluating the performance and energy-efficiency of different dataflows. MAESTRO is actively developed by the Synergy Lab at Georgia Institute of Technology. For more details about MAESTRO, please visit the following links.

• MAESTRO Website
• MAESTRO Docs

Codebase

Updates

May 26th, 2021

We updated the hardware description file, added off-chip bandwidth added as constraint.

We added a validation folder with data for Eyeriss and MAERI from MICRO 2019 paper.

Oct 13th, 2020

We added a direct support for GEMM layers. For more information, please take a look at here.

May 13th, 2020

We updated the naming convention of mappings and the directory structure of data folder.

Oct 14th, 2019

Latest codebase released along with MAESTRO MICRO 2019 paper.

Maintainers

• Felix (Sheng-Chun) Kao ([email protected])
• Geonhwa Jeong ([email protected])
• Tushar Krishna ([email protected])

Technical Contributors

• Hyoukjun Kwon (Georgia Tech, now at Facebook Reality Labs): Main developer (core framework and functionalities)
• Prasanth Chatarasi (Georgia Tech, now at IBM Research): APIs + interface to mapping optimizers.
• Felix (Sheng-Chun) Kao (Georgia Tech): Pytorch frontend + updates to cost-model/interface + GAMMA mapper
• Geonhwa Jeong (Georgia Tech): Keras frontend + debugging + website maintainer.
• Saurabh Malik (Georgia Tech, now at Microsoft): Jupyter Notebooks demo + website.

Citations

@inproceedings{maestro_micro2019,
  author    = {Hyoukjun Kwon and
               Prasanth Chatarasi and
               Michael Pellauer and
               Angshuman Parashar and
               Vivek Sarkar and
               Tushar Krishna},
  title     = {Understanding Reuse, Performance, and Hardware Cost of {DNN} Dataflow:
               {A} Data-Centric Approach},
  booktitle = {Proceedings of the 52nd Annual {IEEE/ACM} International Symposium
               on Microarchitecture, {MICRO}},
  pages     = {754--768},
  publisher = {{ACM}},
  year      = {2019},
}

@article{maestro_toppicks2020,
  author    = {Hyoukjun Kwon and
               Prasanth Chatarasi and
               Vivek Sarkar and
               Tushar Krishna and
               Michael Pellauer and
               Angshuman Parashar},
  title     = {{MAESTRO:} {A} Data-Centric Approach to Understand Reuse, Performance,
               and Hardware Cost of {DNN} Mappings},
  journal   = {{IEEE} Micro},
  volume    = {40},
  number    = {3},
  pages     = {20--29},
  year      = {2020},
}