Superposed analog memory for vector storage in neural networks

The growing memory demands of artificial neural networks pose a major challenge for energy-efficient computing, particularly for models that rely on large collections of high-dimensional weight and embedding vectors. Conventional memory architectures store these vectors individually, requiring many