待翻譯：FLOPs vs Real Work: The Importance of Replication in AI Efficiency Assessment
AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.14550v1 Announce Type: new Abstract: AI efficiency has recently taken the spotlight in both academy and industry due to massive model scales, high energy demands, and environmental costs. While reporting Floating Point Operations (FLOPs) is a traditional approach for assessing computational costs, the relationship between FLOPs and execution time is not straightforward, as layers with the same number of FLOPs may not have the same execution time because some operations are more easily parallelized than others. This paper sets out to replicate the original experiments from a study that proposed the $\alpha-FLOPs$ estimation formula to verify whether the results remain applicable on newer, more powerful hardware. During the replication process, we identify limitations in the replication materials provided by the original study, including a lack of specific dependency details and transparency regarding regression data. Our results validate the thesis that raw FLOPs alone are not an appropriate metric for execution time, as spatial dimensions remain more easily parallelized than kernel dimensions. However, fine-grained measurements reveal that the relationship is much less straightforward than previously shown, with newer hardware exhibiting instabilities and discontinuities in execution time, including jumps and oscillations, that the $\alpha-FLOPs$ formula generally underestimates. Ultimately, this work validates the empirical findings from the original study but shows negative results when applying the $\alpha-FLOPs$ estimation. We also highlight the critical need for complete and accurate replication packages for research on hardware-dependent efficiency assessment and provide a complete replication package for our implementation to facilitate further study.
AI 服務暫時不可用，以下為來源正文，待恢復後補全翻譯。
--> [Submitted on 30 Apr 2026] Title:FLOPs vs Real Work: The Importance of Replication in AI Efficiency Assessment View a PDF of the paper titled FLOPs vs Real Work: The Importance of Replication in AI Efficiency Assessment, by Enrique Barba Roque and Lu\'is Cruz View PDF HTML (experimental) Abstract:AI efficiency has recently taken the spotlight in both academy and industry due to massive model scales, high energy demands, and environmental costs. While reporting Floating Point Operations (FLOPs) is a traditional approach for assessing computational costs, the relationship between FLOPs and execution time is not straightforward, as layers with the same number of FLOPs may not have the same execution time because some operations are more easily parallelized than others. This paper sets out to replicate the original experiments from a study that proposed the $\alpha-FLOPs$ estimation formula to verify whether the results remain applicable on newer, more powerful hardware. During the replication process, we identify limitations in the replication materials provided by the original study, including a lack of specific dependency details and transparency regarding regression data. Our results validate the thesis that raw FLOPs alone are not an appropriate metric for execution time, as spatial dimensions remain more easily parallelized than kernel dimensions. However, fine-grained measurements reveal that the relationship is much less straightforward than previously shown, with newer hardware exhibiting instabilities and discontinuities in execution time, including jumps and oscillations, that the $\alpha-FLOPs$ formula generally underestimates. Ultimately, this work validates the empirical findings from the original study but shows negative results when applying the $\alpha-FLOPs$ estimation. We also highlight the critical need for complete and accurate replication packages for research on hardware-dependent efficiency assessment and provide a complete replication package for our implementation to facilitate further study. Subjects: Artificial Intelligence (cs.AI); Performance (cs.PF) Cite as: arXiv:2608.14550 [cs.AI] (or arXiv:2608.14550v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.14550 arXiv-issued DOI via DataCite Submission history From: Enrique Barba Roque [view email] [v1] Thu, 30 Apr 2026 14:15:58 UTC (281 KB) Full-text links: Access Paper: View a PDF of the paper titled FLOPs vs Real Work: The Importance of Replication in AI Efficiency Assessment, by Enrique Barba Roque and Lu\'is Cruz View PDF HTML (experimental) TeX Source view license Current browse context: cs.AI new | recent | 2026-08 Change to browse by: cs cs.PF References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)