RoPIM: A Processing-in-Memory Architecture for Accelerating Rotary Positional Embedding in Transformer Models
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DOI: 10.1109/lca.2025.3535470
Abstract: The emergence of attention-based Transformer models, such as GPT, BERT, and LLaMA, has revolutionized Natural Language Processing (NLP) by significantly improving performance across a wide range of applications. A critical factor driving these improvements is… read more here.
Keywords: architecture; transformer models; ropim processing; positional embedding ... See more keywords