Flexibility in AI Chips
Flexibility in AI chip design is crucial for adapting to evolving workloads, as demonstrated by the early decisions to support a wide range of operations beyond just convolutional neural networks. The emergence of transformers as a dominant workload underscores the importance of this adaptability. Furthermore, scaling models effectively involves various forms of parallelism, including data, model, tensor, and pipeline parallelism, which are essential for harnessing the full power of training accelerators.In this clip
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Super Data Science: ML & AI Podcast with Jon Krohn
691: A.I. Accelerators: Hardware Specialized for Deep Learning — with Ron Diamant
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