⚡ TL;DR: Can you instantly say how many times faster your model could run on your hardware? SOLAR derives the theoretical best-case runtime (Speed-of-Light) automatically and with validation, straight from PyTorch/JAX code. Title: SOLAR: AI-Powered Speed-of-Light Performance Analysis URL: 📌 Key points ・🤖 An LLM translates code into an IR, then a "generate-then-verify" loop checks it by numerically comparing outputs against the original ・🧮 A deterministic backend derives compute and memory traffic in closed form from just the einsum subscripts ・🎚 Multi-fidelity roofline bounds at three levels: unfused, fused, and cache-aware ・✅ 100% operator coverage on KernelBench's 270 problems with zero SOL violations (existing FLOP counters hit 75-84%) ・🚀 Headroom reaches 54.6x at L3, with fusion analysis surfacing another 7.8x of room ・🦾 All three robotics models on Jetson Thor were memory-bound; 500Hz control needs 19.7x current bandwidth ・🔄 Inverse roofline back-solves the minimum hardware specs needed to hit a latency target 🌍 Takeaway: Blending the flexibility of generative AI with the rigor of analytical math, it's a practical foundation for accelerating performance tuning and hardware selection without physical access. #DeepLearning# #Performance#