自律機械向けデータフローアクセラレータアーキテクチャ
Dataflow Accelerator Architecture for Autonomous Machine Computing
自律機械の計算需要を分析し、古典的データフロー原理を現代化したDAAアーキテクチャを提案した論文。
著者: Shaoshan Liu, Yuhao Zhu, Bo Yu, Jean-Luc Gaudiot, Guang R. Gao
分類: cs.AR, cs.AI, cs.RO
原文アブストラクト
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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