FLOPs vs Real Work: The Importance of Replication in AI Efficiency Assessment
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 $α-FLOPs$ estimation formula to verify whether the results remain applicable on newer, more powerful hardware. During the replica...
| Authors | Enrique Barba Roque, Luís Cruz |
|---|---|
| Primary category | cs.AI |
| Published | 30 April 2026 |
| Last updated at source | 30 April 2026 |
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