A preprint describes an analog memory architecture that stores multiple high-dimensional vectors in superposed form, aiming to reduce the number of memory cells needed for neural-network weights and embeddings. The authors say they demonstrate vector storage and retrieval.

The work explores a memory-density approach for energy-efficient computing. Its results concern the experimental architecture in the paper and do not establish a general replacement for conventional digital memory.