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ACCT: Enabling Long-Context Transformer Reasoning Under Memory Constraints
In the rapidly evolving field of artificial intelligence, enabling transformer models to process long-context sequences efficiently remains a significant challenge due to high memory consumption and computational complexity. Traditional transformer architectures such as GPT and BERT rely on full sel
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