Citing AccelForge#

Please cite all of the following papers if you use this work. This work is the combination of the following:

  • CiMLoop: The architecture and component specification.

  • Fast & Fusiest: The multi-Einsum mapper.

  • LoopTree: The mapping specification.

  • LoopForest: The mapspace specification.

  • Turbo-Charged: The single-Einsum mapper (and an essential first step for Fast & Fusiest).

They are available as the following:

\cite{cimloop, fast_fusiest, turbo_charged, looptree, loopforest}

@INPROCEEDINGS{cimloop,
author={Andrulis, Tanner and Emer, Joel S. and Sze, Vivienne},
booktitle={2024 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)},
title={CiMLoop: A Flexible, Accurate, and Fast Compute-In-Memory Modeling Tool},
year={2024},
volume={},
number={},
pages={10-23},
keywords={Performance evaluation;Accuracy;Computational modeling;Computer architecture;Artificial neural networks;In-memory computing;Data models;Compute-In-Memory;Processing-In-Memory;Analog;Deep Neural Networks;Systems;Hardware;Modeling;Open-Source},
doi={10.1109/ISPASS61541.2024.00012}}

@INPROCEEDINGS{looptree,
author={Gilbert, Michael and Wu, Yannan Nellie and Parashar, Angshuman and Sze, Vivienne and Emer, Joel S.},
booktitle={2023 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)},
title={LoopTree: Enabling Exploration of Fused-layer Dataflow Accelerators},
year={2023},
volume={},
number={},
pages={316-318},
keywords={Deep learning;Analytical models;Systematics;Neural networks;Bandwidth;Software;Energy efficiency;analytical modeling;layer fusion;accelerators},
doi={10.1109/ISPASS57527.2023.00038}}

TODO: More citations

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