arXiv:2504.20198v2 Announce Type: replace-cross Abstract: This work presents a comprehensive evaluation of neural network graph compilers across heterogeneous hardware platforms, addressing the critical gap between theoretical optimization techniques and practical deployment scenarios. We demonstrate how vendor-specific optimizations can invalidate relative performance comparisons between architectural archetypes, with performance advantages sometimes completely reversing after compilation. Our systematic analysis reveals that graph compilers exhibit performance patterns highly dependent on bo
The proliferation of diverse AI hardware and the increasing complexity of AI models necessitate more sophisticated software layers for efficient deployment and performance optimization, particularly at the edge.
This work directly addresses crucial performance bottlenecks for AI deployment across heterogeneous hardware, impacting the scalability and efficiency of AI systems from cloud to edge.
The understanding of how neural graph compilers influence AI model performance on varied hardware is evolving, highlighting that compiler-specific optimizations can override inherent architectural advantages.
- · AI software developers
- · Cloud providers
- · Edge AI hardware manufacturers
- · AI accelerator companies
- · Generic AI optimization tools
- · Hardware vendors relying solely on raw compute
- · Organizations without compiler expertise
Improved performance and efficiency for AI models deployed on diverse hardware.
Increased demand for specialized compiler engineers and AI infrastructure software.
Enhanced competition in the AI hardware market as compiler optimization becomes a key differentiator, potentially leading to more fragmented but specialized AI ecosystems.
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Read at arXiv cs.LG