FPGA Implementation of a Face Detector using Neural Networks
Bibliographic record
Abstract
The study implemented an FPGA-based face detector using neural networks. We used a floating point arithmetic unit (FPU) to represent the number system. The FPU provides dynamic range and reduces the bits of arithmetic unit more than fixed point method. These features led to reduction in the memory so that it is efficient for neural networks system with large size data bits. In this system, an FPGA-based face detector using neural networks takes 1.7 ms to process one frame to be 38 times faster than 50 ms of PC (Pentium4, 1.4 GHz). This speed gives a chance to have better picture than PC process and could be named "hardware accelerator". The implemented FPGA-based face detector using neural networks also required 33% of total area in FPGA (Xilinx XC3S1500) which could be applied to a stand-alone system. In order to determine the bits of FPU, we also examined how representation errors affect a detection rate. The arithmetic unit occupies 84%, occupying most of the available area. Therefore bits reduction is needed not only for memory but also for FPU and system size. Reduction from FPU 32 bits (IEEE 754 single precision) to 24 bits reduced the size of memory and arithmetic units by 25%, having only 0.32% deterioration in the detection rate
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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) | 0.004 | 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".