Ramu

RAMu, short for RAM augmentation unit, is a term used in discussions of memory-augmented computing to describe a hardware-software concept for augmenting a computing system's memory with fast, persistent storage and an integrated retrieval mechanism. RAMu aims to provide rapid access to recently used data while maintaining a durable store of longer-term knowledge, enabling AI systems to fetch relevant information without repeatedly querying external databases or enlarging model parameters.

Typical RAMu designs envisage a tiered memory architecture combining volatile RAM for immediate data, non-volatile memory for persistence and large-scale knowledge, and a retrieval engine that can perform similarity search or content-addressable lookups. The retrieval engine uses embeddings or keys to locate items and returns ranked results to the processor or model. A controller coordinates ingestion, indexing, eviction, and updates to ensure consistency between memory and model state. Data freshness, versioning, and access controls are common concerns.

RAMu is proposed to reduce inference latency for knowledge-intensive tasks, improve resilience to information gaps, and allow models to adapt to new information without full retraining. It is discussed in contexts such as language models with external memory, robotic systems requiring persistent world knowledge, and any scenario where fast recall of structured or unstructured data is advantageous.

As a term, RAMu is not a standardized specification or widely adopted product. It appears in theoretical discussions, speculative design, and some early research proposals rather than established hardware platforms. Related concepts include memory-augmented neural networks, differentiable external memory, and vector-based retrieval systems.