SOLAR: AI-Powered Speed-of-Light Performance Analysis
SOLAR is a framework that automatically derives validated speed-of-light performance bounds from PyTorch and JAX source code. Combining an LLM frontend with deterministic components, it provides unfused, fused, and cache-aware SOL bounds with multi-fidelity analysis. Evaluated on KernelBench, JAX/Flax models, and robotics workloads, it enables headroom analysis, optimization discovery, cross-platform exploration, and inverse-roofline hardware provisioning.
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[Submitted on 24 Jun 2026]
Title:SOLAR: AI-Powered Speed-of-Light Performance Analysis
View a PDF of the paper titled SOLAR: AI-Powered Speed-of-Light Performance Analysis, by Qijing Huang and 11 other authors
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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 code. SOLAR leverages both generative and deterministic components in its flow: an LLM frontend translates any source programs into an executable Affine Loop IR, validated by output comparison; a deterministic flow lifts the IR into an einsum graph; and an analytical backend computes unfused, fused, and cache-aware SOL bounds. SOLAR provides comprehensive operator and language coverage, produces validated bounds with zero observed SOL violations, and offers multi-fidelity analysis that tightens bounds and surfaces optimization insights. We evaluate SOLAR across KernelBench, JAX/Flax models, and robotics workloads. These experiments demonstrate four use cases: headroom analysis at multiple fidelity levels, identifying optimization opportunities, cross-platform exploration, and inverse-roofline hardware provisioning.
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR); Multiagent Systems (cs.MA); Performance (cs.PF)
Cite as: arXiv:2606.26383 [cs.LG]
(or arXiv:2606.26383v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.26383
arXiv-issued DOI via DataCite
Submission history
From: Qijing Huang [view email] [v1] Wed, 24 Jun 2026 21:09:29 UTC (193 KB)
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