Extending Data Flow Architectures for Convolutional Neural Networks to Object Detection and Multiple FPGAs
Bibliographic record
Abstract
This thesis augments and extends the state-of-the-art CNN inference accelerator for FPGAs, HPIPE. We first focus on the infrastructure of the accelerator, where we build an extra hardware unit to implement the Sigmoid function and automated unit tests to validate the functionality of the accelerator. We then study how to leverage the AI-optimized Stratix 10 NX FPGAs to achieve up to 7X speedup for convolution operations. Next, we extend HPIPE by integrating it with a hardware-friendly non-maximum suppression (NMS) unit to accelerate object detection and provide the highest-performing single-shot detection-based (SSD-based) object detection accelerator for FPGAs. Finally, we build an automated CAD flow to partition CNNs across multiple FPGAs that communicate via 100 Gb Ethernet. We show through a prototype system that doubling the number of FPGAs results in 2X performance improvement on three CNNs: MobileNet-V1, MobileNet-V2, and ResNet-50.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".