A new technical paper titled "Analog in-memory computing attention mechanism for fast and energy-efficient large language models" was published by researchers at Forschungszentrum Jülich and RWTH Aachen.
Abstract
"Transformer networks, driven by self-attention, are central to large language models. In generative transformers, self-attention uses cache memory to store token projec...
» read more
