Dataflow Accelerator Architecture for Autonomous Machine Computing

Commercial autonomous machines is a thriving sector, one that is likely the next ubiquitous computing platform, after Personal Computers (PC), cloud computing, and mobile computing. Nevertheless, a suitable computing substrate for autonomous machines is missing, and many companies are forced to develop ad hoc computing solutions that are neither principled nor extensible. By analyzing the demands of autonomous machine computing, this article proposes Dataflow Accelerator Architecture (DAA), a modern instantiation of the classic dataflow principle, that matches the characteristics of autonomous machine software.

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Publication Details

Published
2024-10-27
DOI
https://doi.org/10.1145/3676536.3697112
Citations
1
Primary Topic
Parallel Computing and Optimization Techniques
Type
article
Field-Weighted Citation Impact
0.49
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article

Dataflow Accelerator Architecture for Autonomous Machine Computing

Jean‐Luc Gaudiot, Yuhao Zhu, Yu Bo, Guang R. Gao et al.
1 citations
Parallel Computing and Optimization Techniques
0.49
article

Dataflow Accelerator Architecture for Autonomous Machine Computing

Jean‐Luc Gaudiot, Yuhao Zhu, Yu Bo, Guang R. Gao, Shaoshan Liu
article en
1 citations

Abstract

Commercial autonomous machines is a thriving sector, one that is likely the next ubiquitous computing platform, after Personal Computers (PC), cloud computing, and mobile computing. Nevertheless, a suitable computing substrate for autonomous machines is missing, and many companies are forced to develop ad hoc computing solutions that are neither principled nor extensible. By analyzing the demands of autonomous machine computing, this article proposes Dataflow Accelerator Architecture (DAA), a modern instantiation of the classic dataflow principle, that matches the characteristics of autonomous machine software.

University of California, Irvine (US), Shenzhen Academy of Robotics (CN), University of Rochester (US), University of Delaware (US)
Affordable and clean energy
Openalex Percentile: Top 31%
Parallel Computing and Optimization Techniques
0.49
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