Computational Graph Optimization with Program Synthesis Techniques

Abstract

The optimization of computational graphs is a critical step in deploying machine learning models, particularly deep neural networks. Traditional manual optimization methods are labor-intensive, require deep domain expertise, and are prone to human error. This paper proposes a novel approach leveraging program synthesis techniques to automate the process of computational graph optimization. The core idea is to treat computational graph optimization as a program synthesis problem, where a synthesis engine generates efficient execution plans based on the graph's structure, target hardware specifications, and performance constraints. We introduce a framework that translates performance objectives (e.g., minimizing latency, maximizing throughput) into formal synthesis targets. The system employs a combination of constraint solving, search algorithms, and potentially machine learning to explore the space of possible execution plans and identify the optimal one. This approach promises to significantly reduce the time and effort required for optimization, improve the accuracy of the resulting plans, and facilitate the deployment of complex models on diverse hardware platforms. The key contribution lies in applying program synthesis to a traditionally manual and complex optimization task, opening new avenues for automated and adaptive model deployment.