A Handel-C Implementation of the Back-Propagation Algorithm on Field Programmable Gate Arrays
Bibliographic record
Abstract
General purpose processors (GPPs) and ASICs have traditionally been the common means for building and implementing artificial neural networks (ANNs). However such computing paradigms suffer from the constant need of establishing a trade-off between flexibility and performance. Due to the technological advance in the development of programmable logic devices, field programmable gate arrays (FPGAs) have become attractive for realizing ANNs. FPGAs have shown to exhibit excellent flexibility in terms of reprogramming the same hardware and at the same time achieving good performance by enabling parallel computation. In this paper various implementations of ANNs on FPGAs are investigated and compared. The research described in this paper proposes three partially parallel architectures and a fully parallel architecture to realize the back-propagation algorithm on an FPGA. The proposed designs are coded in Handel-C and functionally verified by synthesizing them on a Virtex2000e FPGA chip. The partially parallel architectures and the fully parallel architecture are found to be 2.25 and 4 times faster than the software implementation respectively for different benchmarks.
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 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".