RedMule: A mixed-precision matrix–matrix operation engine for flexible and energy-efficient on-chip linear algebra and TinyML training acceleration-Reference-Cited by-同舟云学术

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RedMule: A mixed-precision matrix–matrix operation engine for flexible and energy-efficient on-chip linear algebra and TinyML training acceleration

Published:2023-12 Issue: Volume:149 Page:122-135
ISSN:0167-739X
Container-title:Future Generation Computer Systems
language:en
Short-container-title:Future Generation Computer Systems

Author:

Tortorella Yvan^ORCID,Bertaccini Luca,Benini Luca,Rossi Davide,Conti Francesco

Publisher

Elsevier BV

Subject

Computer Networks and Communications,Hardware and Architecture,Software

Reference55 articles.

1. The emergence of edge computing;Satyanarayanan;Computer,2017

2. A unified framework for numerical and combinatorial computing;Gilbert;Comput. Sci. Eng.,2008

3. High-performance graph algorithms from parallel sparse matrices;Gilbert,2008

4. Semiring frameworks and algorithms for shortest-distance problems;Mohri;J. Autom. Lang. Combin.,2002

5. Generalizing matrix multiplication for efficient computations on modern computers;Sedukhin,2012

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. TinyWolf — Efficient on-device TinyML training for IoT using enhanced Grey Wolf Optimization;Internet of Things;2024-12

2. Enhancing Memory Utilization For On-Device Training of TinyML Models Utilizing Enhanced Grey Wolf Optimization Pushing State-of-the-Art Limits - TinyWolf;SSRN Electronic Journal;2023

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