数据流网络的松弛激活分析——一种用于机器学习和实时调度的时钟演算
Relaxed activation analysis of dataflow networks - A clock calculus for machine learning and real-time scheduling
浏览论文内容
中文总结 AI 辅助
研究针对现有Lustre时钟演算不适用于机器学习训练算法控制模式表示的问题，提出保守扩展，解决表达式繁琐和编译效率低的问题，以利于在反应式应用中嵌入机器学习模型。
中文摘要 AI 辅助
先前工作表明，Lustre语言的简单数据流原语能够自然、语义明确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时钟演算负责静态确定诸如活性（无死锁）和静态内存界限等重要属性。然而现有时钟演算专为嵌入式控制应用定制，不适用于训练算法中常见控制模式的表示，导致表达式繁琐且编译效率低。我们提出对Lustre时钟演算的保守扩展以解决此限制，便于在反应式应用中嵌入机器学习模型。
英文摘要
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.
发表机构
- CHIPS JU Shift2SDV project（CHIPS JU Shift2SDV 项目）
机构由 AI 辅助整理，请以论文原文为准。
