Open-Source Heterogeneous SoCs for AI: The PULP Platform Experience

Fuente: arXiv
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Autori principali: Conti, Francesco, Garofalo, Angelo, Rossi, Davide, Tagliavini, Giuseppe, Benini, Luca
Natura: Preprint
Pubblicazione: 2024
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author Conti, Francesco
Garofalo, Angelo
Rossi, Davide
Tagliavini, Giuseppe
Benini, Luca
author_facet Conti, Francesco
Garofalo, Angelo
Rossi, Davide
Tagliavini, Giuseppe
Benini, Luca
contents Since 2013, the PULP (Parallel Ultra-Low Power) Platform project has been one of the most active and successful initiatives in designing research IPs and releasing them as open-source. Its portfolio now ranges from processor cores to network-on-chips, peripherals, SoC templates, and full hardware accelerators. In this article, we focus on the PULP experience designing heterogeneous AI acceleration SoCs - an endeavour encompassing SoC architecture definition; development, verification, and integration of acceleration IPs; front- and back-end VLSI design; testing; development of AI deployment software.
format Preprint
id arxiv_https___arxiv_org_abs_2412_20391
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Open-Source Heterogeneous SoCs for AI: The PULP Platform Experience
Conti, Francesco
Garofalo, Angelo
Rossi, Davide
Tagliavini, Giuseppe
Benini, Luca
Hardware Architecture
Neural and Evolutionary Computing
Signal Processing
Since 2013, the PULP (Parallel Ultra-Low Power) Platform project has been one of the most active and successful initiatives in designing research IPs and releasing them as open-source. Its portfolio now ranges from processor cores to network-on-chips, peripherals, SoC templates, and full hardware accelerators. In this article, we focus on the PULP experience designing heterogeneous AI acceleration SoCs - an endeavour encompassing SoC architecture definition; development, verification, and integration of acceleration IPs; front- and back-end VLSI design; testing; development of AI deployment software.
title Open-Source Heterogeneous SoCs for AI: The PULP Platform Experience
topic Hardware Architecture
Neural and Evolutionary Computing
Signal Processing
url https://arxiv.org/abs/2412.20391