XTC, A Research Platform for Optimizing AI Workload Operators
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Abstract (arXiv)
Achieving high efficiency on AI operators demands precise control over computation and data movement. However, existing scheduling languages are locked into specific compiler ecosystems, preventing fair comparison, reuse, and evaluation across frameworks. No unified interface currently decouples scheduling specification from code generation and measurement. We introduce XTC, a platform that unifies scheduling and performance evaluation across compilers. With its common API and reproducible measurement framework, XTC enables portable experimentation and accelerates research on optimization strategies.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 23 references that are in the map. First posted on arXiv 18 Dec 2025.
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References · 7
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2020Ansor: Generating High-Performance Tensor Programs for Deep Learningcited by 201
- 2018Learning to Optimize Tensor Programscited by 140
- 2020MLIR: A Compiler Infrastructure for the End of Moore's Lawcited by 65
- 2022TinyIREE: An ML Execution Environment for Embedded Systems from Compilation to Deploymentcited by 12
- 2024The MLIR Transform Dialect. Your compiler is more powerful than you thinkcited by 3
- 2024Exo 2: Growing a Scheduling Languagecited by 3
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