arXiv:2606.09312v1 Announce Type: new Abstract: Tensor program optimization is essential for modern machine learning systems, but its search space is enormous. Existing auto-schedulers reduce measurement cost with learned cost models, yet they usually evaluate each candidate as a static code snapshot, ignoring the schedule trajectory that produced it. This makes them insensitive to action dependencies and vulnerable to superficial code variations. We propose a \emph{world-model-inspired} evaluator that models schedule evaluation as action-conditioned latent dynamics over program states. Starti
The increasing complexity and demand for AI models necessitate more efficient underlying compute, pushing research towards advanced compiler and optimization techniques.
Improving tensor program optimization directly contributes to more efficient and powerful AI systems, impacting virtually all sectors reliant on machine learning.
This research introduces a new approach to compiler optimization by modeling schedule evaluation as latent dynamics, potentially leading to significantly faster and more resource-efficient AI model training and inference.
- · AI hardware manufacturers
- · Cloud AI providers
- · Machine learning researchers
- · Any industry using complex AI models
- · Inefficient AI accelerators
- · AI developers without access to advanced optimization tools
More efficient compilation and execution of AI workloads.
Reduced computational costs for AI development and deployment, making advanced AI more accessible.
Acceleration of AI research and deployment timelines due to reduced resource constraints, potentially bringing forward new AI capabilities.
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Read at arXiv cs.LG