arXiv:2606.26344v1 Announce Type: cross Abstract: Writing high performance kernels for AI accelerators requires deep expertise in tiling, instruction selection, data layout, and operator fusion placing a significant burden on programmers. In this paper, we focus on tile based AI accelerator programs and present Axon, a synthesizing superoptimizer for tensor programs: it uses program synthesis to automatically generate target instructions from semantics specifications, and explores semantically equivalent program variants to select the best performing kernel empirically. Axon discovers algebrai
The increasing complexity and performance demands of AI models are driving the need for more efficient and optimized tensor program execution on specialized accelerators.
This development addresses a critical bottleneck in AI development, making high-performance AI more accessible and accelerating computational efficiency for complex models.
The burden on programmers to manually optimize AI kernels is significantly reduced, allowing for faster development and deployment of advanced AI applications.
- · AI accelerator manufacturers
- · AI application developers
- · Cloud computing providers
- · Large language model developers
- · Companies relying on outdated manual optimization techniques
Increased performance and efficiency for AI workloads on specialized hardware.
Lower compute costs for training and inference, democratizing access to powerful AI.
Acceleration in the development of increasingly complex and multimodal AI models previously limited by computational constraints.
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