SOLAR: AI-Powered Speed-of-Light Performance Analysis
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 code. SOLAR leverages both generative and deterministic components in its flow: an LLM frontend translates any source programs into an executable...
| Authors | Qijing Huang, Sana Damani, Zhifan Ye et al. |
|---|---|
| Primary category | cs.LG |
| Published | 24 June 2026 |
| Last updated at source | 22 September 2026 |
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