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2025-06-04 13:39:50

This https://arxiv.org/abs/2504.05424 has been replaced.
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Speculative Automated Refactoring of Imperative Deep Learning Programs to Graph Execution
Efficiency is essential to support ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code -- supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. Though hybrid approach…