SYCL-based Acceleration of Canny Edge Detector Algorithm Using DPC++
Bibliographic record
Abstract
Edge detection is a fundamental task in image processing which has proliferated into various fields. This paper presents a SYCL-based Data Parallel C++ (dpc++) implementation and evaluation of Canny Edge Detection (CED) in CPU-GPU and CPU-FPGA heterogeneous computing platforms. The CED accelerator was implemented and evaluated on two state-of-the-art Intel FPGAs, Arria 10 GX-1150 and Stratix 10 SX-2800 and an Intel GPU, UHD P630 with the Intel's CPU, Xeon Gold-6128. Along with a mathematical optimization, design optimizations specific to parallel computing were leveraged for efficiency and speedup. The evaluation of the implementation reports speedups of 11.6X and 11.7X for Arria 10 and Stratix 10 FPGAs respectively with respect to the CPU -only implementation. Also, both FPGAs completed the algorithm more than 4 times faster than the GPU. Furthermore, Arria 10 FPGA is 31 times more energy efficient than the GPU. Also compared to related research, significantly better speedup results were achieved by our im-plementation. With 0.214 milliseconds execution time to detect edges in an image with size 256x256 pixels, the recommended dimension for real-time image processing applications, our CPU-FPGA implementation is best suited for real-time applications
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".