DARKSIDE: A Heterogeneous RISC-V Compute Cluster for Extreme-Edge On-Chip DNN Inference and Training-Reference-Cited by-同舟云学术

DARKSIDE: A Heterogeneous RISC-V Compute Cluster for Extreme-Edge On-Chip DNN Inference and Training

Published:2022 Issue: Volume:2 Page:231-243
ISSN:2644-1349
Container-title:IEEE Open Journal of the Solid-State Circuits Society
language:
Short-container-title:IEEE Open J. Solid-State Circuits Soc.

Author:

Garofalo Angelo¹^ORCID,Tortorella Yvan¹^ORCID,Perotti Matteo²,Valente Luca¹^ORCID,Nadalini Alessandro¹,Benini Luca¹,Rossi Davide¹^ORCID,Conti Francesco¹^ORCID

Affiliation:

1. Department of Electrical, Electronic and Information Engineering, University of Bologna, Bologna, Italy

2. IIS Integrated Systems Laboratory, ETH Zürich, Zürich, Switzerland

Funder

EU Horizon 2020 Research and Innovation through the European Pilot Project

WiPLASH

ECSEL Horizon 2020 Project AI4DI

GreenWaves Technologies

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/8782712/9733783/09903915.pdf?arnumber=9903915

Reference36 articles.

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2. GVSoC: A Highly Configurable, Fast and Accurate Full-Platform Simulator for RISC-V based IoT Processors

3. NEURA ghe

4. A transprecision floating-point platform for ultra-low power computing

5. Dustin: A 16-cores parallel ultra-low-power cluster with 2b-to-32b fully flexible bit-precision and vector Lockstep execution mode;ottavi;arXiv 2201 08656,2022

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