Systematic Framework Leveraging Early Assessment of DNN Reliability for Efficient and Reliable FPGA-Based Inference Deployment
Bibliographic record
Abstract
Deep Neural Networks (DNNs) deployed on edge AI hardware accelerators are increasingly susceptible to transient faults caused by environmental disturbances, often resulting in soft errors that degrade system reliability. In this work, we present a comprehensive and extensible framework that enables early evaluation of DNN reliability purely at the Python software level, supporting arbitrary user-defined models and datasets. The framework systematically incorporates the effects of aforementioned optimization techniques and introduces integrated error mitigation strategies to assess their effectiveness pre-hardware deployment. To bridge software-level evaluation with hardware implementation, we extend our framework to a hardware/software codesign platform featuring a configurable systolic array on FPGA. This array, enhanced with built-in fault mitigation mechanisms, accelerates convolution and linear layers, while an ARM CPU core completes the remaining inference tasks. The proposed system achieves resilient DNN inference with a 2.2% increase in FPGA LUT utilization (for LeNet-5 on MNIST). By enabling early-stage analysis and optimization, our approach facilitates robust and resource-efficient DNN deployment for edge AI applications.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".