FPGA defect tolerance: impact of granularity.
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
As device sizes shrink, FPGAs are increasingly prone to manufacturing defects. The ability to tolerate multiple defects is anticipated to be very important at 45nm and beyond. One possible approach to this growing problem is to add redundancy to create a defect-tolerant FPGA architecture. Using area, delay and yield metrics, this paper compares two redundancy strategies: a coarse-grain approach using spare rows and columns and a fine-grain approach using spare wires. For low defect levels and large array sizes, the coarse-grain approach offers a lower area overhead, but it is relatively intolerant to an increase in defect count. In contrast, the fine-grain approach has a fixed overhead of up to 50%, but the architecture can tolerate an increasing number of defects as array size grows. To achieve a similar level of yield recovery, the coarse-grain approach requires an area overhead in excess of 100%
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