Hardened-TC: A Low-cost Reliability Solution for CNNs Run by Modern GPUs
Bibliographic record
Abstract
Convolutional neural networks (CNNs) have become a compelling solution for various applications such as image classification, object detection, and climate studies. The introduction of tensor cores (TCs) in NVIDIA GPUs targets the acceleration of neural network computations. While there have been numerous studies on the performance and power of TCs, the reliability of TCs received little attention, particularly at the hardware level. Recently, CNNs have been deployed into safety-critical applications such as self-driving cars. Soft errors caused by high-energy particles are concerning as they can lead to catastrophic failures in CNN systems. The high power and area cost of traditional methods for building resilient systems such as triple modular redundancy make selective protection techniques attractive. We propose a hardware-based selective protection mechanism where vulnerable components of a CNN are implemented on resilient TCs. Resizing transistors in hardened TCs offers a low-cost solution to boost the reliability of CNNs. We also propose a precision-aware approach to optimize hardened TCs further. CNNs are resilient to approximation and quite often do not require full precision. By dropping transistor resizing in the least significant bits of network parameters, we are able to offer a low-cost reliability solution for CNNs implemented on TCs while maintaining the accuracy of the original network.
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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 | 0.001 | 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".