Ubiquitous memory augmentation via mobile multimodal embedding system

Forgetting is inevitable in human memory. Recently, multimodal embedding models have been proposed to vectorize multimodal reality into a unified embedding space. Once generated, these embeddings allow mobile users to quickly retrieve relevant information, effectively augmenting their memory. However, as the model's capacity increases, its resource consumption also rises. The resulting slow throughput and significant computational resource requirements hinder its deployment on mobile devices. In this paper, we present Reminisce, an efficient on-device multimodal embedding system that enables high-throughput embedding and precise retrieval on resource-constrained mobile devices. The core design draws inspiration from the memory functions of the human brain, utilizing coarse-grained embeddings to identify likely candidates, which are then refined through query-driven fine-grained retrieval. A series of algorithm-hardware orchestrated optimizations automatically navigates this process and strengthen the embedding quality. Experiments show that Reminisce provides high-quality embedding representation with high throughput while operating silently in the background with negligible memory usage and reduced energy consumption.