An energy-efficient, fast FPGA hardware architecture for OpenCV-Compatible object detection
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.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".