Zulberti-Highly_Parameterised_CGRA_for_DSE_of_ML_Onboard.pdf (376.26 kB)
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Highly-Parameterised CGRA Architecture for Design Space Exploration of Machine Learning Applications Onboard Satellites

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posted on 2023-09-08, 21:49 authored by Luca ZulbertiLuca Zulberti, Matteo Monopoli, Pietro Nannipieri, Luca Fanucci

This work presents a highly parameterised CGRA-based accelerator that we developed for an extensive Design Space Exploration activity on design parameters. The description starts from the CGRA building blocks, the Functional Units, and progresses towards the top level of the architecture, represented by the Node component, which is composed of an NxM matrix of Processing Elements. For each level of the hierarchy, we describe the HDL design parameters affecting the run-time reconfigurability of the accelerator, delving deeper into the functionality of the architecture. Outcomes are reported after synthesis on TSMC 40nm standard-cell technology.


This work is partially supported by the Italian Ministry of University and Research (MUR) in the framework of the FoReLab project (Departments of Excellence).

This work is partially supported by the European Space Agency (ESA) in the framework of the Open Space Innovation Platform (OSIP) co-funded research (Contract No. 4000137503).


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Submitting Author's Institution

University of Pisa

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  • Italy