CascadeCNN: Pushing the Performance Limits of Quantisation in Convolutional Neural Networks

This work presents CascadeCNN, an automated toolflow that pushes the quantisation limits of any given CNN model, aiming to perform high-throughput inference. A two-stage architecture tailored for any given CNN-FPGA pair is generated, consisting of a low- and high-precision unit in a cascade... (read more)

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Methods used in the Paper


METHOD TYPE
1x1 Convolution
Convolutions
Convolution
Convolutions
Local Response Normalization
Normalization
Grouped Convolution
Convolutions
ReLU
Activation Functions
Dropout
Regularization
Dense Connections
Feedforward Networks
Max Pooling
Pooling Operations
Softmax
Output Functions
AlexNet
Convolutional Neural Networks