For the CNN presented, we only approximate layers Conv2 and Conv3. This is because layer Conv4 has a 1 × 1 filter size and so would not benefit much from our speedup schemes. We also don’t approximate Conv1 due to the fact that it acts on raw pixels from natural images – the f...
The focus of this paper is speeding up the evaluation of convolutional neural networks. While delivering impressive results across a range of computer vision and machine learning tasks, these networks are computationally demanding, limiting their deployability. Convolutional layers generally consume the bul...
Convolutional layers generally consume the bulk of the processing time, and so in this work we present two simple schemes for drastically speeding up these layers. This is achieved by exploiting cross-channel or filter redundancy to construct a low rank basis of filters that are rank-1 in...
During the learning step of a CNN, only three parameters have to be set by the user: the network size (number of layers, filters and units), the learning rate, and the augmentation values. During the application step, two parameters are optimized, the binarization threshold and the minimum ...
To solve the hardware deployment problem caused by the vast demanding computational complexity of convolutional layers and limited hardware resources for the hardware network inference, a look-up table (LUT)-based convolution architecture built on a field-programmable ...
Layer conv2Dtranspose:<class 'tensorflow.python.keras.layers.convolutional.Conv2DTranspose'> is not supported. You can quantize this layer by passing a `tfmot.quantization.keras.QuantizeConfig` instance to the `quantize_annotate_layer` API. Most of the official example quantized models of Edge TPU...
5.1. Applying Deformable Convolution on Different Number of Last Few Layers Results of using deformable convolution in the last 1, 2, 3, and 6 convolutional layers (of 3×3 filter) inResNet-101 Both 3 and 6 deformable convolutions are also good. Finally,3 is chosen...
net = trainnet(data,layers,lossFcn,options); Note This topic outlines some commonly used training options. The options listed here are only a subset. For a complete list, seetrainingOptions. Solvers The solver is the algorithm that the training function uses to optimize the learnable parameters....
GECCO is a lightweight image classifier based on single MLP and graph convolutional layers. We find that our model can achieve up to 16x better latency than other state-of-the-art models. The paper for our model can be found at https://arxiv.org/abs/2402
Results show that for example, an overall compression rates of 2x can be achieved on a compact ResNet-32 model on the CIFAR-10 dataset, with only a negligible loss of 2% of the network accuracy, while up to 11x compression rates can be achieved on specific layers with negligible accuracy ...