A Fully Parallel and Scalable Implementation of a Hopfield Neural Network on the SHARC-NET Supercomputer
Bibliographic record
Abstract
Artificial neural networks (ANN) are an established area of artificial intelligence (AI) and computer science. ANNs have been used in a number of ways for research and industrial projects. However, despite ANN research spanning many years, the typical implementation is a single threaded programming model. This paper presents a fully parallel implementation of a Hopfield neural network using a supercomputer. The goal of this project is to develop a core learning unit capable of enormous range of scaling ability over a large number of nodes in a supercomputer. Furthermore, we integrate techniques that minimize the dependencies on any particular topology thus making it easier to port to other supercomputing environments. Ideally, other SHARC-net users extend these ideas and conduct research using the tools developed in this project. This paper provides an outline of the issues associated with the development of this artificial neural network on SHARC-net, the benefits of such work, the difficulties encountered and future directions.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".