SOLAR: An Automated Framework for Speed-of-Light Performance Analysis of Deep Learning Models

[Submitted on 24 Jun 2026]

7d ago· 2 min readenInsight

technology machine learning programming performance analysis

Summary

This paper introduces SOLAR, an automated framework for computing Speed-of-Light (SOL) analysis — the theoretical minimum execution time for deep-learning models on target hardware. SOLAR combines an LLM frontend that translates PyTorch and JAX source code into an executable Affine Loop IR, a deterministic flow that lifts the IR into an einsum graph, and an analytical backend that computes unfused, fused, and cache-aware SOL bounds. The framework is evaluated across KernelBench, JAX/Flax models, and robotics workloads, demonstrating use cases including headroom analysis, optimization opportunity identification, cross-platform exploration, and inverse-roofline hardware provisioning.

Source

bskySOLAR: An Automated Framework for Speed-of-Light Performance Analysis of Deep Learning Modelsarxiv.org

Key quotes

· 4 pulled

How fast could a deep-learning model run on target hardware, and how far is today's implementation from that limit?

Speed-of-Light (SOL) analysis answers them by computing a workload's theoretical minimum execution time on a given architecture.

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.

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.

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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 w

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