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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 6·5 min read
RooflineBench: A Benchmarking Framework for On-Device LLMs via Roofline Analysis
TL;DR
RooflineBench is a benchmarking framework that utilizes Roofline analysis to evaluate the performance of Small Language Models (SLMs) on resource-constrained edge devices. It introduces the Relative Inference Potential metric, revealing how model depth and sequence length impact efficiency across different hardware architectures.
✦ Why It Matters
Evaluate your LLMs using RooflineBench to optimize performance on edge devices today.
Key Takeaways
- Use RooflineBench to benchmark LLMs on edge hardware.
- Consider sequence length when evaluating model performance.
- Address efficiency traps caused by hardware heterogeneity.
- Implement Multi-head Latent Attention to improve inference potential.
How It Works
RooflineBench integrates the Roofline model to assess the performance of LLMs by analyzing operational intensity (OI) against hardware constraints. It defines an inference-potential region that allows for the comparison of different models on the same hardware, focusing on how changes in sequence length and model depth affect efficiency.