Matrix multiplication is a fundamental operation in engineering, physics, and machine learning, but improving its efficiency has been a long-standing challenge. Now, researchers at DeepMind are using reinforcement learning to discover new, faster multiplication algorithms, surpassing traditional methods like Strassen and Coppersmith-Winograd.
These AI-optimized algorithms can significantly reduce computation time for large-scale data processing tasks, such as image processing, deep learning training, and scientific simulations. By exploring multiple strategies and selecting near-optimal solutions, reinforcement learning provides a novel approach to algorithm discovery, potentially reshaping computational mathematics.
If these techniques continue to improve, they could lead to faster AI models, more efficient simulations, and major advancements in numerical computing.
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Fawzi, A., Balog, M., Huang, A., Hubert, T., Romera-Paredes, B., Barekatain, M., Novikov, A., R Ruiz, F. J., Schrittwieser, J., Swirszcz, G., Silver, D., Hassabis, D., & Kohli, P. (2022). Discovering faster matrix multiplication algorithms with reinforcement learning. Nature, 610(7930), 47–53. https://doi.org/10.1038/s41586-022-05172-4
Does this mean we can train deep learning models faster?