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

Karthekeyan Periasamy*, Jelin Leslin*, Aleksi Korsman*, Lingyun Yao*, Martin Andraud*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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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.

Original languageEnglish
Title of host publication2024 22nd IEEE Interregional NEWCAS Conference, NEWCAS 2024
PublisherIEEE
Pages21-25
Number of pages5
ISBN (Electronic)979-8-3503-6175-9
DOIs
Publication statusPublished - 2024
MoE publication typeA4 Conference publication
EventIEEE International New Circuits and Systems Conference - Sherbrooke, Canada
Duration: 16 Jun 202419 Jun 2024
Conference number: 22

Publication series

NameIEEE International New Circuits and Systems Conference
ISSN (Electronic)2474-9672

Conference

ConferenceIEEE International New Circuits and Systems Conference
Abbreviated titleNEWCAS
Country/TerritoryCanada
CitySherbrooke
Period16/06/202419/06/2024

Keywords

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

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