SparseCores in Tensor Processing Units (TPUs) are particularly beneficial for AI applications that heavily rely on embeddings, such as Deep Learning Recommendation Models (DLRMs). These models are widely used in advertising, search ranking, and platforms like YouTube. SparseCores accelerate the processing of embeddings by transforming large categorical spaces into smaller dense spaces, which is crucial for recommendation systems. For instance, TPU v4's SparseCores make it 3X faster than TPU v3 for recommendation models and up to 5-30X faster than CPU-based systems[1][3].
Additionally, SparseCores are advantageous in ranking and advanced recommendation workloads, where ultra-large embeddings are common. This is further enhanced in newer TPU models like Trillium, which integrate third-generation SparseCores to optimize performance for these specific tasks[7]. Overall, any AI application that involves complex embeddings or sparse data structures can significantly benefit from the capabilities of SparseCores in TPUs.
Citations:[1] https://www.kdnuggets.com/2023/04/introducing-tpu-v4-googles-cutting-edge-supercomputer-large-language-models.html
[2] https://www.wevolver.com/article/tpu-vs-gpu-in-ai-a-comprehensive-guide-to-their-roles-and-impact-on-artificial-intelligence
[3] https://www.vibranium.sg/post/introducing-tpu-v4-google-s-supercomputer-for-large-language-models
[4] https://www.datacamp.com/blog/tpu-vs-gpu-ai
[5] https://cloud.google.com/blog/products/ai-machine-learning/introducing-cloud-tpu-v5p-and-ai-hypercomputer
[6] https://eng.snap.com/training-models-with-tpus
[7] https://futurumgroup.com/insights/the-future-of-ai-infrastructure-unpacking-googles-trillium-tpus/
[8] https://www.zdnet.com/article/5-reasons-why-googles-trillium-could-transform-ai-and-cloud-computing-and-2-obstacles/
[9] https://cloud.google.com/tpu
[10] https://arxiv.org/pdf/2309.08918.pdf