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This research proposes a conservative extension to the Lustre clock calculus, enabling more efficient compilation of machine learning models within reactive dataflow networks by addressing limitations in representing training algorithm control patterns. By bridging static analysis techniques from embedded systems with modern ML architectures, this work facilitates safer integration of complex AI models into real-time scheduling environments without relying on hype or general software updates.
Relaxed activation analysis of dataflow networks - A clock calculus for machine learning and real-time scheduling
arXiv:2607.21797v1 Announce Type: cross 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.
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.
Programming Languages (cs.PL); Machine Learning (cs.LG)
arXiv:2607.21797 [cs.PL]
(or arXiv:2607.21797v1 [cs.PL] for this version)
https://doi.org/10.48550/arXiv.2607.21797 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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