HDDB: Efficient In-Storage SQL Database Search Using Hyperdimensional Computing on Ferroelectric NAND Flash

2025-11-22

AI Summary

To address the high energy consumption and latency of in-memory database search under high raw bit error rates (BER), this paper proposes an in-memory computing SQL processing architecture leveraging hyperdimensional computing (HDC) and ferroelectric NAND (FeNAND). We innovatively design an HDC encoding scheme tailored for relational tables, mapping SQL predicate filtering and aggregation operations to high-dimensional vector similarity computations. By exploiting FeNAND’s multi-level cell (MLC) capability, our architecture enables bit-level parallel in-memory computation without explicit error correction, tolerating up to 10% storage errors robustly. Evaluated on TPC-DS fact tables, our approach achieves up to 80.6× lower latency and 12,636× lower energy consumption compared to state-of-the-art CPU- and GPU-based database engines. This work presents the first demonstration of robust and efficient in-situ database query execution on highly noisy, non-volatile memory.

Technology Category

• Search and Optimization: Distributed Search
• Data Mining & Knowledge Management: Intelligent Query Processing
• Machine Learning: Hardware-aware ML

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Abstract

Hyperdimensional Computing (HDC) encodes information and data into high-dimensional distributed vectors that can be manipulated using simple bitwise operations and similarity searches, offering parallelism, low-precision hardware friendliness, and strong robustness to noise. These properties are a natural fit for SQL database workloads dominated by predicate evaluation and scans, which demand low energy and low latency over large fact tables. Notably, HDC's noise-tolerance maps well onto emerging ferroelectric NAND (FeNAND) memories, which provide ultra-high density and in-storage compute capability but suffer from elevated raw bit-error rates. In this work, we propose HDDB, a hardware-software co-design that combines HDC with FeNAND multi-level cells (MLC) to perform in-storage SQL predicate evaluation and analytics with massive parallelism and minimal data movement. Particularly, we introduce novel HDC encoding techniques for standard SQL data tables and formulate predicate-based filtering and aggregation as highly efficient HDC operations that can happen in-storage. By exploiting the intrinsic redundancy of HDC, HDDB maintains correct predicate and decode outcomes under substantial device noise (up to 10% randomly corrupted TLC cells) without explicit error-correction overheads. Experiments on TPC-DS fact tables show that HDDB achieves up to 80.6x lower latency and 12,636x lower energy consumption compared to conventional CPU/GPU SQL database engines, suggesting that HDDB provides a practical substrate for noise-robust, memory-centric database processing.

Problem

• Enabling efficient SQL database search using hyperdimensional computing on noisy FeNAND flash
• Performing in-storage SQL predicate evaluation with minimal data movement and energy consumption
• Maintaining correct query results under high device noise without error-correction overhead

Innovation

• Hyperdimensional Computing for SQL database search
• Hardware-software co-design with FeNAND memory
• Noise-tolerant in-storage processing without error correction