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Found 6 papers, 2 papers with code
Hundred-Kilobyte Lookup Tables for Efficient Single-Image Super-Resolution
Conventional super-resolution (SR) schemes make heavy use of convolutional neural networks (CNNs), which involve intensive multiply-accumulate (MAC) operations, and require specialized hardware such as graphics processing units.
Lite it fly: An All-Deformable-Butterfly Network
Most deep neural networks (DNNs) consist fundamentally of convolutional and/or fully connected layers, wherein the linear transform can be cast as the product between a filter matrix and a data matrix obtained by arranging feature tensors into columns.
PECAN: A Product-Quantized Content Addressable Memory Network
A novel deep neural network (DNN) architecture is proposed wherein the filtering and linear transform are realized solely with product quantization (PQ).
Deformable Butterfly: A Highly Structured and Sparse Linear Transform
We introduce a new kind of linear transform named Deformable Butterfly (DeBut) that generalizes the conventional butterfly matrices and can be adapted to various input-output dimensions.
Exploiting Elasticity in Tensor Ranks for Compressing Neural Networks
Elasticities in depth, width, kernel size and resolution have been explored in compressing deep neural networks (DNNs).
EZCrop: Energy-Zoned Channels for Robust Output Pruning
Recent results have revealed an interesting observation in a trained convolutional neural network (CNN), namely, the rank of a feature map channel matrix remains surprisingly constant despite the input images.
