Vector-Centric Machine Learning Systems: A Cross-Stack Approach

TL;DR

This paper presents a cross-stack approach to improving the efficiency of vector-centric ML systems, including algorithms, systems, and hardware optimizations for RAG, vector search, and recommender systems.

Contribution

It introduces novel algorithms, system designs, and hardware co-design strategies specifically targeting the efficiency of vector-based ML applications.

Findings

Enhanced RAG serving efficiency with PipeRAG, RAGO, and Chameleon.

Optimized vector search algorithms FANNS and Falcon for hardware efficiency.

Improved recommender system performance with MicroRec and FleetRec.

Abstract

Today, two major trends are shaping the evolution of ML systems. First, modern AI systems are becoming increasingly complex, often integrating components beyond the model itself. A notable example is Retrieval-Augmented Generation (RAG), which incorporates not only multiple models but also vector databases, leading to heterogeneity in both system components and underlying hardware. Second, with the end of Moore's Law, achieving high system efficiency is no longer feasible without accounting for the rapid evolution of the hardware landscape. Building on the observations above, this thesis adopts a cross-stack approach to improving ML system efficiency, presenting solutions that span algorithms, systems, and hardware. First, it introduces several pioneering works about RAG serving efficiency across the computing stack. PipeRAG focuses on algorithm-level improvements, RAGO introduces system-level optimizations, and Chameleon explores heterogeneous accelerator systems for RAG. Second, this thesis investigates algorithm-hardware co-design for vector search. Specifically, FANNS and Falcon optimize quantization-based and graph-based vector search, the two most popular paradigms of retrieval algorithms. Third, this thesis addresses the serving efficiency of recommender systems, another example of vector-centric ML systems, where the memory-intensive lookup operations on embedding vector tables often represent a major performance bottleneck. MicroRec and FleetRec propose solutions at the hardware and system levels, respectively, optimizing both data movement and computation to enhance the efficiency of large-scale recommender models.