A Survey of FPGA-based 3D CNN Accelerators and Hardware-aware Algorithmic Optimizations
Bibliographic record
Abstract
3D Convolutional Neural Networks (3D CNNs) can outperform 2D CNNs on several tasks, including action recognition, video captioning, abnormal event detection, and medical image interpretation. Compared to 2D CNNs, 3D CNNs have a larger number of parameters and higher computational complexity. For this reason, researchers have focused on designing efficient 3D CNN architectures and hardware accelerators. The purpose of this survey is to serve as a guide to recent work on 3D CNNs for action recognition with a focus on FPGA-based accelerators and hardware-aware algorithmic optimizations. We provide an overview of the state-of-the-art in 3D CNN architectures as well as the action recognition datasets used for training and testing the architectures. A performance comparison of 2D versus 3D CNNs on two datasets (Sports-1M and UCF101) is included. We explore the designs of FPGA-based accelerators and compare them in terms of achieved throughput, resource utilization, and power. We survey current methods of optimization for 3D CNN architectures, which are meant to reduce the number of parameters and the memory requirements and to facilitate their deployment on FPGAs. Finally, we highlight current challenges and potential areas of improvement in acceleration of 3D CNNs on FPGA platforms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".