FT-CNN: Algorithm-Based Fault Tolerance for Convolutional Neural Networks

Convolutional neural networks (CNNs) are becoming more and more important for solving challenging and critical problems in many fields. CNN inference applications have been deployed in safety-critical systems, which may suffer from soft errors caused by high-energy particles, high temperature, or abnormal voltage... (read more)

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Methods used in the Paper


METHOD TYPE
Residual Connection
Skip Connections
VGG
Convolutional Neural Networks
Bottleneck Residual Block
Skip Connection Blocks
Residual Block
Skip Connection Blocks
Kaiming Initialization
Initialization
ResNet
Convolutional Neural Networks
Average Pooling
Pooling Operations
Global Average Pooling
Pooling Operations
1x1 Convolution
Convolutions
Batch Normalization
Normalization
Darknet-19
Convolutional Neural Networks
Local Response Normalization
Normalization
Grouped Convolution
Convolutions
ReLU
Activation Functions
Dropout
Regularization
Dense Connections
Feedforward Networks
Max Pooling
Pooling Operations
Softmax
Output Functions
YOLOv2
Object Detection Models
AlexNet
Convolutional Neural Networks
Convolution
Convolutions