A High-Performance RISC-V SoC with a DNN Accelerator for Energy Applications

As in many applications, the energy sector is witnessing the migration of artificial intelligence (AI) algorithms from the cloud to the edge. This paper introduces a System-on-Chip (SoC) designed for energy control units (ECUs) and data concentrators (DCs) to support edge computing acceleration. The SoC is implemented on the Chipyard platform, utilizing the open-source out-of-order processor BOOM and the software-friendly accelerator Gemmini. Fabricated using a 22nm process, the core achieves a normal operating frequency of 1GHz and occupies a die area of 22.99mm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>.

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Published
2024-12-27
DOI
https://doi.org/10.1109/eiecc64539.2024.10929464
Primary Topic
Radiation Effects in Electronics
Type
article
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article

A High-Performance RISC-V SoC with a DNN Accelerator for Energy Applications

Le‐Wei Li, Xinyu Liu, Yang Ren, Nianxiong Tan
Radiation Effects in Electronics
article

A High-Performance RISC-V SoC with a DNN Accelerator for Energy Applications

Le‐Wei Li, Xinyu Liu, Yang Ren, Nianxiong Tan
article en

Abstract

As in many applications, the energy sector is witnessing the migration of artificial intelligence (AI) algorithms from the cloud to the edge. This paper introduces a System-on-Chip (SoC) designed for energy control units (ECUs) and data concentrators (DCs) to support edge computing acceleration. The SoC is implemented on the Chipyard platform, utilizing the open-source out-of-order processor BOOM and the software-friendly accelerator Gemmini. Fabricated using a 22nm process, the core achieves a normal operating frequency of 1GHz and occupies a die area of 22.99mm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>.

Zhejiang University (CN)
Affordable and clean energy
Openalex Percentile: Top 73%
Radiation Effects in Electronics
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A High-Performance RISC-V SoC with a DNN Accelerator for Energy Applications — Le‐Wei Li, Xinyu Liu, et al. · (2024) | TGRS Research Map | TGRS