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RooflineBench: A Benchmarking Framework for On-Device LLMs via Roofline Analysis
arXiv – CS AI|Zhen Bi, Xueshu Chen, Luoyang Sun, Yuhang Yao, Qing Shen, Jungang Lou, Cheng Deng||4 views
🤖AI Summary
Researchers introduce RooflineBench, a framework for measuring performance capabilities of Small Language Models on edge devices using operational intensity analysis. The study reveals that sequence length significantly impacts performance, model depth causes efficiency regression, and structural improvements like Multi-head Latent Attention can unlock better hardware utilization.
Key Takeaways
- →RooflineBench framework enables systematic performance comparison of language models across different edge hardware platforms.
- →Sequence length variations significantly influence both performance and operational intensity in on-device language models.
- →Model depth increases cause critical regression in operational intensity, reducing efficiency.
- →Hardware heterogeneity creates efficiency traps that limit language model performance on edge devices.
- →Multi-head Latent Attention structural refinements can effectively improve inference potential across various hardware substrates.
#edge-ai#language-models#hardware-optimization#benchmarking#inference-efficiency#roofline-analysis#small-language-models#on-device-ai
Read Original →via arXiv – CS AI
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