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Authors: Sasindu Wijeratne 1 ; Ta-Yang Wang 1 ; Rajgopal Kannan 2 and Viktor Prasanna 1

Affiliations: 1 University of Southern California, Los Angeles, U.S.A. ; 2 US Army Research Lab, Los Angeles, U.S.A.

Keyword(s): Tensor Decomposition, MTTKRP, Memory Controller, FPGA.

Abstract: Tensor decomposition has become an essential tool in many data science applications. Sparse Matricized Tensor Times Khatri-Rao Product (MTTKRP) is the pivotal kernel in tensor decomposition algorithms that decompose higher-order real-world large tensors into multiple matrices. Accelerating MTTKRP can speed up the tensor decomposition process immensely. Sparse MTTKRP is a challenging kernel to accelerate due to its irregular memory access characteristics. Implementing accelerators on Field Programmable Gate Array (FPGA) for kernels such as MTTKRP is attractive due to the energy efficiency and the inherent parallelism of FPGA. This paper explores the opportunities, key challenges, and an approach for designing a custom memory controller on FPGA for MTTKRP while exploring the parameter space of such a custom memory controller.

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Paper citation in several formats:
Wijeratne, S.; Wang, T.; Kannan, R. and Prasanna, V. (2022). Towards Programmable Memory Controller for Tensor Decomposition. In Proceedings of the 11th International Conference on Data Science, Technology and Applications - DATA; ISBN 978-989-758-583-8; ISSN 2184-285X, SciTePress, pages 468-475. DOI: 10.5220/0011301200003269

@conference{data22,
author={Sasindu Wijeratne. and Ta{-}Yang Wang. and Rajgopal Kannan. and Viktor Prasanna.},
title={Towards Programmable Memory Controller for Tensor Decomposition},
booktitle={Proceedings of the 11th International Conference on Data Science, Technology and Applications - DATA},
year={2022},
pages={468-475},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011301200003269},
isbn={978-989-758-583-8},
issn={2184-285X},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Data Science, Technology and Applications - DATA
TI - Towards Programmable Memory Controller for Tensor Decomposition
SN - 978-989-758-583-8
IS - 2184-285X
AU - Wijeratne, S.
AU - Wang, T.
AU - Kannan, R.
AU - Prasanna, V.
PY - 2022
SP - 468
EP - 475
DO - 10.5220/0011301200003269
PB - SciTePress

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