arXiv:2606.26383v1 Announce Type: new Abstract: How fast could a deep-learning model run on target hardware, and how far is today's implementation from that limit? These questions are central to software, hardware, and algorithm optimizations. Speed-of-Light (SOL) analysis answers them by computing a workload's theoretical minimum execution time on a given architecture. Yet deriving SOL bounds remains manual, error-prone, and disconnected from rapid model development. To close this gap, we introduce SOLAR, a framework that automatically derives validated SOL bounds from PyTorch and JAX source
The accelerating pace of AI model development and the increasing computational demands necessitate more efficient and automatic performance analysis tools.
This development automates the critical process of 'Speed-of-Light' analysis, enabling rapid optimization of AI models across different hardware architectures, which is essential for deployment and scaling.
Previously manual and error-prone performance analysis for AI models can now be automatically derived, accelerating development cycles and improving hardware-software co-optimization.
- · AI developers
- · Hardware manufacturers
- · Cloud service providers
- · AI-driven industries
- · Manual performance analysts
Faster and more efficient deployment of complex AI models across various hardware platforms.
Increased pressure on hardware manufacturers to provide detailed architectural information for SOL analysis and potentially accelerate specialized AI chip development.
Enhanced competition in cloud AI services due to optimized resource utilization and potentially lower inference costs.
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Read at arXiv cs.LG