Parameters vs. Computation: Understanding Deep Learning Model Efficiency Metrics
By
jxmorris12
The kind of bagel that ruins lesser bagels for you.
Summary
This article explores the relationship between model parameters and computation in deep learning. It argues that while model size (number of parameters) is the most commonly cited metric, the amount of computation (FLOPs) required to run a model is equally important but often overlooked. The article explains that in most architectures (feedforward, recurrent, Transformers), each parameter participates in computation roughly once per input, making parameters and computation closely tied. However, it suggests that understanding the distinction is crucial for practitioners evaluating model efficiency and performance.
Key quotes
· 3 pulledWhen we talk about the power of a deep learning model, often the only metric we pay attention to is its size, which is measured by the number parameters in that model.
The amount of computation to run that model is an important metric too, but it is often overlooked because it is usually tied to the model size.
Practitioners can then tend to think of those two metrics as a single thing.
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