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Making Sense of Big Data
Model Compression with TensorFlow Lite: A Look into Reducing Model Size
Common pitfalls you have to know to apply model compression seamlessly
Why is Model Compression important?
A significant problem in the arms race to produce more accurate models is complexity, which leads to the problem of size. These models are usually huge and resource-intensive, which leads to greater space and time consumption. (Takes up more space in memory and slower in prediction as compared to smaller models)
The Problem of Model Size
A large model size is a common byproduct when attempting to push the limits of model accuracy in predicting unseen data in deep learning applications. For example, with more nodes, we can detect subtler features in the dataset. However, for project requirements such as using AI in embedded systems that depend on fast predictions, we are limited by the available computational resources. Furthermore, prevailing edge devices do not have networking capabilities, as such, we are not able to utilize cloud computing. This results in the inability to use massive models which would take too long to get meaningful predictions.
