This AI Paper Demonstrates An End-to-End Training Flow on An Large Language Model LLM-13 Billion GPT-Using Sparsity And Dataflow

GPT-4 says:
Researchers are exploring the use of sparse approaches in machine learning to reduce computational intensity and mimic human brain connections. To address the challenges of power, cost, and training time, next-generation hardware must offer flexibility, programmability, and efficiency. Various computational frameworks have been proposed, but their full capabilities in handling sparse and dense applications remain to be explored. The study by SambaNova Systems demonstrates the successful incorporation of sparsity in an end-to-end training cycle using a 13B parameter GPT model, achieving equivalent accuracy metrics.
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