Reconfigurable acceleration for database systems: Taxonomy, techniques, and research challenges
Bibliographic record
Abstract
Database query processing and optimization are critical components of modern database management systems (DBMS) that efficiently process user queries. In big data application scenarios, the movement of large volumes of data influences performance, power efficiency, and reliability, which are the three essential aspects of a computing system. Large-scale data centers require an exceptionally efficient server and storage infrastructure. The systems currently employed for managing and processing big data are increasingly showing inefficiency, both in terms of energy usage and scalability, primarily due to the constraints imposed by existing CPU architectures. A significant challenge in Database Management Systems (DBMS) is the growing disparity between the speeds of processors and memory access, which results in notable performance bottlenecks. This paper presents a comprehensive survey of reconfigurable acceleration in database systems, offering a structured taxonomy that categorizes existing work based on query types, integration models, and hardware/software co-design strategies. We examine key acceleration techniques across relational operators, indexing, join algorithms, and compression, highlighting their trade-offs in performance, scalability, and adaptability. Furthermore, we identify current limitations in programmability, data movement, and workload variability, and outline open research challenges including dynamic reconfiguration, hybrid architectures, and compiler support. This taxonomy-driven perspective aims to guide both researchers and practitioners in navigating the design space and pushing the boundaries of FPGA-accelerated data processing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".