Parallel FPGA-based implementation of scatter search
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
Scatter Search [1] is an effective and established population-based meta-heuristic that has been used to solve a variety of hard optimization problems. However, like most population-based meta-heuristics, the time required to find high-quality solutions can become prohibitive as problem sizes grow. In this paper, we present a hardware implementation of scatter search on a Field-Programmable Gate-Array (FPGA). Our objective is to improve the runtime of scatter search by exploiting the potentially massive performance benefits that are available through the native parallelism in hardware. When implementing scatter search we employ Handel-C [2] - a programming language specifically designed to enable software developers to easily synthesize C-like programs into synchronous hardware. As far as we know, this is the first time that scatter search has been implemented in hardware (of any form). Our empirical results show that by effectively exploiting data parallelism and pipelining a 28x speedup over software can be achieved.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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