Neural Networks have proven themselves very adept at solving a wide variety of problems, in particular they accel at image processing. However, it remains unknown how well they perform under memory errors. This thesis focuses on the robustness of neural networks under memory errors, specifically single event upset style errors where single bits flip in a network's trained parameters. The main goal of these experiments is to determine if different neural network architectures are more robust than others. Initial experiments show that MLPs are more robust than CNNs. Within MLPs, deeper MLPs are more robust and for CNNs larger kernels are more robust. Additionally, the CNNs displayed bimodal failure behavior, where memory errors would either not affect the performance of the network, or they would degrade its performance to be on par with random guessing. VGG16, ResNet50, and InceptionV3 were also tested for their robustness. ResNet50 and InceptionV3 were both more robust than VGG16. This could be due to their use of Batch Normalization or the fact that ResNet50 and InceptionV3 both use shortcut connections in their hidden layers. After determining which networks were most robust, some estimated error rates from neutrons were calculated for space environments to determine if these architectures were robust enough to survive. It was determined that large MLPs, ResNet50, and InceptionV3 could survive in Low Earth Orbit on commercial memory technology and only use software error correction. / Master of Science / Neural networks are a new kind of algorithm that are revolutionizing the field of computer vision. Neural networks can be used to detect and classify objects in pictures or videos with accuracy on par with human performance. Neural networks achieve such good performance after a long training process during which many parameters are adjusted until the network can correctly identify objects such as cats, dogs, trucks, and more. These trained parameters are then stored in a computers memory and then recalled whenever the neural network is used for a computer vision task. Some computer vision tasks are safety critical, such as a self-driving car’s pedestrian detector. An error in that detector could lead to loss of life, so neural networks must be robust against a wide variety of errors. This thesis will focus on a specific kind of error: bit flips in the parameters of a neural networks stored in a computer’s memory. The main goal of these bit flip experiments is to determine if certain kinds of neural networks are more robust than others. Initial experiments show that MLP (Multilayer Perceptions) style networks are more robust than CNNs (Convolutional Neural Network). For MLP style networks, making the network deeper with more layers increases the accuracy and the robustness of the network. However, for the CNNs increasing the depth only increased the accuracy, not the robustness. The robustness of the CNNs displayed an interesting trend of bimodal failure behavior, where memory errors would either not affect the performance of the network, or they would degrade its performance to be on par with random guessing. A second set of experiments were run to focus more on CNN robustness because CNNs are much more capable than MLPs. The second set of experiments focused on the robustness of VGG16, ResNet50, and InceptionV3. These CNNs are all very large and have very good performance on real world datasets such as ImageNet. Bit flip experiments showed that ResNet50 and InceptionV3 were both more robust than VGG16. This could be due to their use of Batch Normalization or the fact that ResNet50 and InceptionV3 both use shortcut connections within their network architecture. However, all three networks still displayed the bimodal failure mode seen previously. After determining which networks were most robust, some estimated error rates were calculated for a real world environment. The chosen environment was the space environment because it naturally causes a high amount of bit flips in memory, so if NASA were to use neural networks on any rovers they would need to make sure the neural networks are robust enough to survive. It was determined that large MLPs, ResNet50, and InceptionV3 could survive in Low Earth Orbit on commercial memory technology and only use software error correction. Using only software error correction will allow satellite makers to build more advanced satellites without paying extra money for radiation-hardened electronics.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/84527 |
| Date | 08 August 2018 |
| Creators | Arechiga, Austin Podoll |
| Contributors | Electrical and Computer Engineering, Michaels, Alan J., Williams, Ryan K., Black, Jonathan T. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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