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Google details RigL algorithm for building more efficient neural networks

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Google LLC today details RigL, an algorithm developed by researchers. It increases hardware efficiency by shrinking the artificial intelligence model.

Neural networks consist of so-called artificial neurons (individual mathematical operations implemented in code) that are linked to each other by software connections. These connections allow artificial neurons to pass data to each other for processing. RigL makes AI software more efficient by fixing common optimization problems in machine learning models. In many cases, there are more connections between neurons than exactly what is needed.

AI model connections effectively act as data paths, and the data that the model normally processes only passes through a subset of those paths. Others are left unused and unnecessarily occupy processor and memory resources. According to Google, RigL removes redundant connections by making strategic adjustments to the neural network structure during the training phase of development.

Google researchers have tested RigL in experiments involving image processing models. You have been given the task of analyzing images that contain different text.

During the model training phase, RigL determined that the AI ​​only had to analyze the foreground text in each image and skip the processing of the background pixels. The background pixels do not contain any useful information. The algorithm then removed the connection used to process the background pixels and added a new, more efficient connection in its place.

The algorithm identifies the neurons that need to be activated during training. This allows the optimization process to take advantage of the most relevant connections, resulting in improved sparse solutions. Deletes some of the connections at regular intervals.

Other than RigL, there are other ways to try to compress the neural network by removing redundant connections. However, these methods have the disadvantage of significantly reducing the accuracy of the compressed model, which limits the actual application. According to Google, RigL achieves higher accuracy than the three most sophisticated alternatives, while consistently requiring less FLOP (and memory footprint) than other methods.

In one test, Google researchers used RigL to remove 80% of the connections on the popular ResNet-50 model. The resulting neural network achieved the same accuracy as the original. In another experiment, researchers reduced the ResNet-50 by 99%, but showed the highest accuracy of 70.55%.

“RigL is useful in three different scenarios. It improves the accuracy of sparse models for deployment purposes and improves the accuracy of large sparse models that can only be trained with a limited number of iterations. [and] Evci and Castro, in combination with sparse primitives, have enabled training of very large sparse models that was not possible otherwise.

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