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GAS: A scheduling primitive dependency analysis-based cost model for tensor program optimization
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DOI：10.1016/j.sysarc.2026.103721.png)
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Abstract
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Automatically generating high-performance tensor programs has become a promising approach for deploying deep neural networks. A key challenge lies in designing an effective cost model to navigate the vast scheduling search space. Existing approaches typically fall into two categories, each with limitations: offline learning cost models rely on large pre-collected datasets, which may be incomplete or device-specific, and online learning cost models depend on handcrafted features, requiring substantial manual effort and expertise.
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