arXiv:2602.20427v2 Announce Type: replace Abstract: Efficient operator scheduling is a fundamental challenge in software compilation and hardware synthesis. While recent differentiable approaches have sought to replace traditional ones like exact solvers or heuristics with gradient-based search, they typically rely on categorical distributions that fail to capture the ordinal nature of time and suffer from a parameter space that scales poorly. In this paper, we propose a novel differentiable framework, GauS, that models operator scheduling as a stochastic relaxation using Gaussian distribution
The continuous drive for efficiency in AI hardware and software systems necessitates advanced optimization techniques, and this research presents a novel approach leveraging Gaussian distributions for scheduling.
Differentiable scheduling optimization is critical for improving the performance and efficiency of AI acceleration hardware, directly impacting the capabilities and costs of advanced computing.
Current approaches for operator scheduling, relying on categorical distributions, will be challenged by new methods that better capture temporal ordinality and scale more effectively.
- · AI hardware manufacturers
- · Cloud computing providers
- · AI model developers
- · Software compilation toolchains
- · Developers reliant on traditional scheduling heuristics
Improved efficiency and speed of AI model training and inference on specialized hardware.
Reduced operational costs for large-scale AI deployments and potentially more complex AI models becoming feasible.
Accelerated development cycles for new AI hardware architectures and optimized software stacks, leading to a faster pace of AI innovation across industries.
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Read at arXiv cs.LG