Abstract: Compute-In-Memory (CiM) is emerging as a promising paradigm to design energy-efficient hardware accelerators for AI, addressing the processor-memory data transfer bottleneck. The popularity ...
Abstract: Attention mechanisms, particularly within Transformer architectures and large language models (LLMs), have revolutionized sequence modeling in machine learning and artificial intelligence ...
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