Hardware acceleration for similarity computations of feature vectors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In Web mining applications, an enormous amount of data needs to be processed quickly and efficiently. Thus, hardware support is crucial to enhance the performance of these operations. In this work, chip-level hardware support for similarity measure calculation is introduced and then extended to similarity matrix computation. Both software and hardware versions of various similarity computations are implemented with a hierarchical platform-based design approach to facilitate component reuse at different levels of abstraction. Software modules are executed on a reconfigurable soft processor core on the same field-programmable gate array (FPGA) as the hardware implementations for the purpose of performance comparison. Preliminary results using parallel hardware are also presented. In addition, performance gain from hardware as opposed to a general-purpose processor is examined. Experimental results show that the performance of similarity computation for a set of feature vectors could be significantly enhanced by using on-chip hardware.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it