scholarly journals Robust Convolutional Neural Networks in SRAM-based FPGAs: a Case Study in Image Classification

2021 ◽  
Vol 16 (2) ◽  
pp. 1-12
Author(s):  
Fabio Benevenuti ◽  
Fernanda Lima Kastensmidt ◽  
Ádria Barros de Oliveira ◽  
Nemitala Added ◽  
Vitor Ângelo Paulino de Aguiar ◽  
...  
Keyword(s):  
Image Classification ◽  
Hardware Implementation ◽  
Fault Injection ◽  
Fine Tuning ◽  
Traffic Sign ◽  
Sign Recognition ◽  
User Data ◽  
Floating Point Number ◽  
Xilinx Fpga

This work discusses the main aspects of vulnerability and degradation of accuracy of an image classification engine implemented into SRAM-based FPGAs under faults. The image classification engine is an all-convolutional neural-network (CNN) trained with a dataset of traffic sign recognition benchmark. The Caffe and Ristretto frameworks were used for CNN training and fine-tuning while the ZynqNet inference engine was adopted as hardware implementation on a Xilinx 28 nm SRAM-based FPGA. The CNN under test was generated using an evolutive approach based on genetic algorithm. The methodologies for qualifying this CNN under faults is presented and both heavy-ions accelerated irradiation and emulated fault injection were performed. To cross validate results from radiation and fault injection, different implementations of the same CNN were tested using reduced arithmetic precision and protection of user data by Hamming codes, in combination with configuration memory healing by the scrubbing mechanism available in Xilinx FPGA. Some of these alternative implementations increased significantly the mission time of the CNN, when compared to the original ZynqNet operating on 32 bits floating point number, and the experiment suggests areas for further improvements on the fault injection methodology in use.

scholarly journals Triplet Loss Network for Unsupervised Domain Adaptation

Algorithms ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 96 ◽  
Author(s):  
Imad Eddine Ibrahim Bekkouch ◽  
Youssef Youssry ◽  
Rustam Gafarov ◽  
Adil Khan ◽  
Asad Masood Khattak
Keyword(s):  
Image Classification ◽  
Domain Adaptation ◽  
Fine Tuning ◽  
Generative Adversarial Networks ◽  
Target Domain ◽  
Traffic Sign ◽  
Linear Discriminant ◽  
Unsupervised Domain Adaptation ◽  
Sign Recognition ◽  
Almost All

Domain adaptation is a sub-field of transfer learning that aims at bridging the dissimilarity gap between different domains by transferring and re-using the knowledge obtained in the source domain to the target domain. Many methods have been proposed to resolve this problem, using techniques such as generative adversarial networks (GAN), but the complexity of such methods makes it hard to use them in different problems, as fine-tuning such networks is usually a time-consuming task. In this paper, we propose a method for unsupervised domain adaptation that is both simple and effective. Our model (referred to as TripNet) harnesses the idea of a discriminator and Linear Discriminant Analysis (LDA) to push the encoder to generate domain-invariant features that are category-informative. At the same time, pseudo-labelling is used for the target data to train the classifier and to bring the same classes from both domains together. We evaluate TripNet against several existing, state-of-the-art methods on three image classification tasks: Digit classification (MNIST, SVHN, and USPC datasets), object recognition (Office31 dataset), and traffic sign recognition (GTSRB and Synthetic Signs datasets). Our experimental results demonstrate that (i) TripNet beats almost all existing methods (having a similar simple model like it) on all of these tasks; and (ii) for models that are significantly more complex (or hard to train) than TripNet, it even beats their performance in some cases. Hence, the results confirm the effectiveness of using TripNet for unsupervised domain adaptation in image classification.

scholarly journals How Deeply to Fine-Tune a Convolutional Neural Network: A Case Study Using a Histopathology Dataset

2020 ◽  
Vol 10 (10) ◽  
pp. 3359 ◽  
Author(s):  
Ibrahem Kandel ◽  
Mauro Castelli
Keyword(s):  
Image Classification ◽  
Transfer Learning ◽  
Medical Image ◽  
Disease Diagnosis ◽  
Fine Tuning ◽  
Medical Image Classification ◽  
Fine Tune ◽  
Entire Network

Accurate classification of medical images is of great importance for correct disease diagnosis. The automation of medical image classification is of great necessity because it can provide a second opinion or even a better classification in case of a shortage of experienced medical staff. Convolutional neural networks (CNN) were introduced to improve the image classification domain by eliminating the need to manually select which features to use to classify images. Training CNN from scratch requires very large annotated datasets that are scarce in the medical field. Transfer learning of CNN weights from another large non-medical dataset can help overcome the problem of medical image scarcity. Transfer learning consists of fine-tuning CNN layers to suit the new dataset. The main questions when using transfer learning are how deeply to fine-tune the network and what difference in generalization that will make. In this paper, all of the experiments were done on two histopathology datasets using three state-of-the-art architectures to systematically study the effect of block-wise fine-tuning of CNN. Results show that fine-tuning the entire network is not always the best option; especially for shallow networks, alternatively fine-tuning the top blocks can save both time and computational power and produce more robust classifiers.

PYNQ FPGA Hardware implementation of LeNet-5-Based Traffic Sign Recognition Application

2021 ◽  
Author(s):  
Amna Maraoui ◽  
Seifeddine Messaoud ◽  
Soulef Bouaafia ◽  
Ahmed Chiheb Ammari ◽  
Lazhar Khriji ◽  
...  
Keyword(s):  
Hardware Implementation ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Sign Recognition

scholarly journals Transfer Learning Based Traffic Sign Recognition Using Inception-v3 Model

2018 ◽  
Vol 47 (3) ◽  
pp. 242-250 ◽  
Author(s):  
Chunmian Lin ◽  
Lin Li ◽  
Wenting Luo ◽  
Kelvin C. P. Wang ◽  
Jiangang Guo
Keyword(s):  
Transfer Learning ◽  
Recognition Performance ◽  
Processing Technique ◽  
Learning Rate ◽  
Training Data ◽  
Fine Tuning ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Sign Recognition ◽  
High Level

Traffic sign recognition is critical for advanced driver assistant system and road infrastructure survey. Traditional traffic sign recognition algorithms can't efficiently recognize traffic signs due to its limitation, yet deep learning-based technique requires huge amount of training data before its use, which is time consuming and labor intensive. In this study, transfer learning-based method is introduced for traffic sign recognition and classification, which significantly reduces the amount of training data and alleviates computation expense using Inception-v3 model. In our experiment, Belgium Traffic Sign Database is chosen and augmented by data pre-processing technique. Subsequently the layer-wise features extracted using different convolution and pooling operations are compared and analyzed. Finally transfer learning-based model is repetitively retrained several times with fine-tuning parameters at different learning rate, and excellent reliability and repeatability are observed based on statistical analysis. The results show that transfer learning model can achieve a high-level recognition performance in traffic sign recognition, which is up to 99.18 % of recognition accuracy at 0.05 learning rate (average accuracy of 99.09 %). This study would be beneficial in other traffic infrastructure recognition such as road lane marking and roadside protection facilities, and so on.

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