Similarity Computation Using Reconfigurable Embedded Hardware
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
Advances in portable devices and location-aware applications have necessitated the research in sophisticated yet small-footprint hardware and software in embedded systems, while the proliferation of the Web and distributed database systems has led to new data mining applications. We are investigating the utilization of reconfigurable hardware, due to its flexibility and performance, for data mining applications in portable and embedded computing. In this work, we introduce a reconfigurable hardware solution using field programmable gate array (FPGA) for similarity matrix computation, a commonly used data structure to represent the computed similarity among a set of feature vectors. Our hardware design can be dynamically reconfigured to accommodate three different similarity measures. A space-time cost analysis of the proposed multiplexer-based approach is presented. Experiments performed on the implemented reconfigurable hardware show encouraging and promising results that warrant further investigation in dynamically reconfigurable FPGA-based hardware for data mining applications.
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