EdgeRAG: Online-Indexed RAG for Edge Devices (2412.21023v2)

Abstract: Deploying Retrieval Augmented Generation (RAG) on resource-constrained edge devices is challenging due to limited memory and processing power. In this work, we propose EdgeRAG which addresses the memory constraint by pruning embeddings within clusters and generating embeddings on-demand during retrieval. To avoid the latency of generating embeddings for large tail clusters, EdgeRAG pre-computes and stores embeddings for these clusters, while adaptively caching remaining embeddings to minimize redundant computations and further optimize latency. The result from BEIR suite shows that EdgeRAG offers significant latency reduction over the baseline IVF index, but with similar generation quality while allowing all of our evaluated datasets to fit into the memory.

• The paper introduces EdgeRAG, a system that optimizes memory for RAG on edge devices using selective embedding pruning, caching, and on-demand generation.
• Evaluation on a Jetson Orin Nano shows EdgeRAG achieves 1.8x faster retrieval latency on average compared to conventional methods while maintaining generation quality.
• This approach allows deploying complex RAG systems directly on resource-limited edge devices like mobile platforms, paving the way for more ubiquitous AI applications.

A Professional Overview of "EdgeRAG: Online-Indexed RAG for Edge Devices"

"EdgeRAG: Online-Indexed RAG for Edge Devices," authored by Korakit Seemakhupt, Sihang Liu, and Samira Khan, presents an innovative approach to deploying Retrieval Augmented Generation (RAG) on resource-constrained edge devices. This work addresses significant challenges posed by the inherent memory and processing constraints of such devices.

Core Contributions

The paper introduces EdgeRAG, a system designed to efficiently manage memory by selectively pruning embeddings within clusters and generating embeddings on-demand during retrieval operations. The proposed technique aims to reduce both computation expense and latency, especially when dealing with large, tail-heavy clusters. Key contributions of the paper include:

1. Memory Optimization Through Pruning: The authors identify that traditional RAG systems face substantial memory overheads, primarily due to the storage of embeddings. By pruning low-usage embeddings and selectively caching them, EdgeRAG manages to reduce memory requirements significantly.
2. On-Demand Embedding Generation: For smaller clusters, embeddings are generated on-the-fly, thus conserving memory. The paper details how this strategy not only saves space but also enhances performance by reducing redundant data retrieval.
3. Adaptive Pre-Computed Embeddings: For clusters with high computational costs or frequent access patterns, the system pre-computes embeddings. This aspect of the system mitigates the latency characteristics associated with the largest clusters.
4. Efficient Use of Limited Memory: By leveraging a combination of pruned and cached embeddings, the system can fit the entire retrieval architecture within the constraints of edge devices like mobile platforms and Nvidia's Jetson Orin Nano.

Performance Evaluation

The evaluation employs six datasets from the BEIR benchmark suite, leveraging Nvidia's Jetson Orin Nano, equipped with 8 GB of shared main memory, reflecting realistic edge usage scenarios. EdgeRAG demonstrates a noteworthy 1.8 times faster retrieval latency over the conventional IVF index on average, and significantly more on larger datasets, while maintaining closely comparable generation quality.

Theoretical and Practical Implications

From a theoretical standpoint, EdgeRAG offers a compelling solution to the vector similarity search overhead, a common bottleneck in resource-constrained environments. Practically, it indicates a path forward for deploying advanced AI capabilities directly on edge devices without necessitating a cloud infrastructure or substantial hardware upgrades.

Speculations for Future Developments

Moving forward, integrating further optimizations such as hardware acceleration using NPUs could amplify EdgeRAG's potential benefits. Moreover, as the pre-processing and clustering algorithms continue to evolve, tailored approaches to clustering may unlock new efficiencies and applicability to larger-scale, heterogeneous datasets.

Conclusion

Overall, the EdgeRAG system showcases a pragmatic approach to memory management and latency reduction for RAG systems on edge devices. Its significant latency improvements and efficient memory use could play a crucial role as edge capacities expand, highlighting the potential for increased deployment of RAG systems in ubiquitous computing scenarios. This work lays the groundwork for ongoing research into adaptive, efficient edge-based AI applications.