Real-time object detection in software with custom vector instructions and algorithm changes
Bibliographic record
Abstract
Real-time vision applications place stringent performance requirements on embedded systems. To meet performance requirements, embedded systems often require hardware implementations. This approach is unfavorable as hardware development can be difficult to debug, time-consuming, and require extensive skill. This paper presents a case study of accelerating face detection, often part of a complex image processing pipeline, using a software/hardware hybrid approach. As a baseline, the algorithm is initially run on a scalar ARM Cortex-A9 application processor found on a Xilinx Zynq device. Next, using a previously designed vector engine implemented in the FPGA fabric, the algorithm is vectorized, using only standard vector instructions, to achieve a 25× speedup. Then, we accelerate the critical inner loops by adding two hardware-assisted custom vector instructions for an additional 10× speedup, yielding 248× speedup over the initial Cortex-A9 baseline. Collectively, the custom instructions require fewer than 800 lines of VHDL code, including comments and blank lines. Compared to previous hardware-only face detection systems, our work is 1.5 to 6.8 times faster. This approach demonstrates that good performance can be obtained from software-only vectorization, and a small amount of custom hardware can provide a significant acceleration boost.
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 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 | 0.001 |
| 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".