Relaxed activation analysis of dataflow networks - A clock calculus for machine learning and real-time scheduling
William Gaudelier Albert Cohen Dumitru Potop Butucaru
作者信息
Abstract
Previous work has shown that the simple dataflow primitives of the Lustre language allow the natural, semantically unambiguous, and compact representation of machine learning (ML) applications, including models featuring complex conditional execution and recurrent state. The Lustre clock calculus is responsible for the static determination of important properties such as liveness (absence of deadlocks) and static memory bounds. Yet existing clock calculi are tailored for embedded control applications. We show they do not cater for the representation of control patterns commonly found in training algorithms, resulting in cumbersome expressions and inefficient compilation. We propose a conservative extension of Lustre's clock calculus addressing this limitation, thereby facilitating the embedding of ML models in reactive applications.引用本文复制引用
William Gaudelier,Albert Cohen,Dumitru Potop Butucaru.Relaxed activation analysis of dataflow networks - A clock calculus for machine learning and real-time scheduling[EB/OL].(2026-07-29)[2026-08-11].https://arxiv.org/abs/2607.21797.学科分类
计算技术、计算机技术
