AutoPC: An Open-Source Framework for Efficient Probabilistic Reasoning on FPGA Hardware

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openAccess

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A4 Artikkeli konferenssijulkaisussa

Date

2024

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Mcode

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Language

en

Pages

5

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Abstract

In the quest for more advanced and energy-efficient edge AI, probabilistic reasoning models can complement or replace deep learning (DL) models, as they are generative, explainable, and trustworthy. However, their hardware implementation and acceleration are still in the early stages compared to DL due to more ad-hoc implementations and challenges translating them into computational steps. This recently evolved with Probabilistic Circuits (PCs), which can be trained with mainstream software and lead to more hardware-efficient inference. Yet, there is currently no single open-source framework dedicated to computing PCs on hardware. In this work, we introduce such a framework called AutoPC, allowing us to (1) compare PCs trained with different PC algorithms to find the most suited, (2) find the optimal resolution required for hardware computation with minimal cost, and (3) automatically generate FPGA hardware for executing PC models with high speed (40-200 GOPS) up to the FPGA capacity. We hope AutoPC serves as a baseline to showcase the possibilities of probabilistic reasoning and broaden the use of PCs.

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Publisher Copyright: © 2024 IEEE.

Keywords

Design Automation, High level HW-Simulation, HW-SW co-design, Probablistic Circuits

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Citation

Periasamy, K, Leslin, J, Korsman, A, Yao, L & Andraud, M 2024, AutoPC: An Open-Source Framework for Efficient Probabilistic Reasoning on FPGA Hardware . in 2024 22nd IEEE Interregional NEWCAS Conference, NEWCAS 2024 . IEEE International New Circuits and Systems Conference, IEEE, pp. 21-25, IEEE International New Circuits and Systems Conference, Sherbrooke, Canada, 16/06/2024 . https://doi.org/10.1109/NewCAS58973.2024.10666359