An energy-efficient, fast FPGA hardware architecture for OpenCV-Compatible object detection
Why this work is in the frame
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Bibliographic record
Abstract
The presence of cameras and powerful computers on modern mobile devices gives rise to the hope that they can perform computer vision tasks as we walk around. However, the computational demand and energy consumption of computer vision tasks such as object detection, recognition and tracking make this challenging. At the same time, a fixed vision hard core on the SoC contained in a mobile chip may not have the flexibility needed to adapt to new situations, or evolve as new algorithms are discovered. This may mean that computer vision on a mobile device is the killer application for FPGAs, and could motivate the inclusion of FPGAs, in some form, within modern smartphones. In this paper we present a novel hardware architecture for object detection, that is bit-for-bit compatible with the object classifiers in the widely-used open source OpenCV computer vision software. The architecture is novel, compared to prior work in this area, in two ways: its memory architecture, and its particular SIMD-type of processing. The implementation, which consists of the full system, not simply the kernel, outperforms a same-generation technology mobile processor by a factor of 59 times, and is 13.5 times more energy-efficient.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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