An FPGA-based face detector using neural network and a scalable floating point unit
Bibliographic record
Abstract
Abstract: The study implemented an FPGA-based face detector using Neural Networks and a scalable Floating Point arithmetic Unit (FPU). The FPU provides dynamic range and reduces the bit of the arithmetic unit more than fixed point method does. These features led to reduction in the memory so that it is efficient for neural networks system with large size data bits. The arithmetic unit occupies 39~45 % of the total neural networks system area. Therefore bits reduction is needed not only for memory but also for a FPU and system size. Reduction from FPU 32 bits (IEEE 754 single precision) to 16 bits reduced the size of memory and arithmetic units by 50%, having only 1.25 % deterioration in the detection rate. In order to determine the least and acceptable bits of the FPU, we examined how representation errors affect a detection rate through the MRRE. The scalable FPU and the error analysis may be useful to determine the details, especially area and speed of FPU for the embedded neural network system.
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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".