Learned Index Acceleration with FPGAs: A SMART Approach
Bibliographic record
Abstract
Indexes in database systems such as B+trees and hash tables are designed for fast data retrieval. These are created on columns of a table and serve as a pointer to map a key to the position of a record on a table. Much research has been conducted on topics related to the faster index. A learned index is one such area of study. Learned index approaches can achieve significant performance improvement over traditional indexing techniques. However, query performance with learned indexes is limited by the constraints imposed by the CPU architecture. FPGAs, on the other hand, offer a suitable alternative by offering energy efficiency and potentially better performance. This paper proposes a new methodology that considers the advantages of the learned index and FPGAs. This methodology is called the Selective Mathematical Operation AcceleRaTion (SMART) approach with an FPGA for an end-to-end acceleration of learned indexes. Being a hybrid between a CPU approach and an FPGA approach, the SMART model of index acceleration achieves higher throughput than the CPU-based implementation while maintaining the data structure storage on the CPU. Our SMART approach accelerated the radix spline (RS) learned index using a single FPGA without any off-chip memory resources. The resulting index, called SMART-RS, achieves an overall speedup of 5.5 × as compared to a CPU-based RS index on the SOSD benchmark datasets.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".