Bibliographic record
Abstract
In an analogue cellular neural networks system, the accuracy of the template and data values will always suffer from various kinds of errors that make analogue CNN chip respond in the erroneous fashion as simulator. How to obtain robust template parameters efficiently in order to guarantee reliable operation is an important issue for the design of analogue CNN circuits. This paper starts from the assumption that digital DT-CNN emulation implemented on FPGA can be used to bridge the implementation gap between CNN system description and analogue realization. In this paper, a digital emulation methodology is described in detail for quickly obtaining the robust templates for analogue CNN system performing the specific operation. And the erroneous analogue CNN chip is simulated by digital DT-CNN implementation on FPGA with network-on-chip approach. The simulation results show that 290 robust templates are generated from 14641 test templates by using this digital emulation methodology and those robust templates can guarantee the correct specific operation with truncating the internal data from full-precision 21 bits (L_max) to 7 bits (L_min).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".