An FPGA Implementation of a Timing-Error Tolerant Discrete Cosine Transform (Abstract Only)
Bibliographic record
Abstract
We present a Discrete Cosine Transform (DCT) unit embedded with Error Detection Sequential (EDS) and Dynamic Voltage Scaling (DVS) circuits to speculatively monitor its noncritical datapaths. This monitoring strategy requires no buffer insertions with only minimal modifications to the existing digital design methodology and is therefore applicable for Field-Programmable Gate Array (FPGA) implementations. The proposed design is implemented in an FPGA. The duty cycles of the constraint clock and the actual clock are differentiated to guide the synthesizer to place the EDS circuits with specific timing margin. The proposed design is tested with two classic images and is able to detect timing errors in the noncritical datapaths due to dynamic process, voltage and temperature (PVT) variations. The DVS circuit correspondingly controls a linear voltage regulator to adjust the supply voltage to the Point of First Failure (PoFF). No actual timing errors are generated, primarily because of the unique speculative characteristic of the proposed monitoring strategy. Our proposed design incurs a 0.3% logic element overhead and 3.5% maximum frequency degradation. By lowering the supply voltage by 8.3%, the proposed design saves up to 16.5% energy when operating at the same frequency as a highly optimized baseline DCT implementation.
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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 | 0.000 |
| 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 | 0.001 |
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".