扩展了Lustre时钟演算，让机器学习模型更高效地嵌入实时系统。
Relaxed activation analysis of dataflow networks - A clock calculus for machine learning and real-time scheduling
- 提出保守扩展的时钟演算，支持复杂训练控制流
- 解决原有演算在训练算法中表达冗余、编译低效问题
- 适合将机器学习模型嵌入反应式系统的研究者
先前研究显示，Lustre语言的数据流原语能自然、语义清晰且紧凑地表示机器学习应用，包括具有复杂条件执行和循环状态的模型。Lustre的时钟演算负责静态确定关键属性，如活性（无死锁）和静态内存上限。然而，现有时钟演算针对嵌入式控制应用设计，不适用于常见于训练算法的控制模式，导致表达繁琐且编译效率低下。本文提出对Lustre时钟演算的保守扩展，解决了这一局限，从而促进机器学习模型在反应式应用中的嵌入。
原文摘要 · Abstract (English)
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.
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