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Partial Convolution based Padding [article]

Guilin Liu, Kevin J. Shih, Ting-Chun Wang, Fitsum A. Reda, Karan Sapra, Zhiding Yu, Andrew Tao, Bryan Catanzaro
2018 arXiv   pre-print
We call it partial convolution based padding, with the intuition that the padded region can be treated as holes and the original input as non-holes.  ...  Specifically, during the convolution operation, the convolution results are re-weighted near image borders based on the ratios between the padded area and the convolution sliding window area.  ...  ResNet50 with partial convolution based padding vs. zero padding.  ... 
arXiv:1811.11718v1 fatcat:uxhvl4h2lje4dbjnocfa3f4vci

Context-aware Padding for Semantic Segmentation [article]

Yu-Hui Huang, Marc Proesmans, Luc Van Gool
2021 arXiv   pre-print
Using context-aware padding, the ResNet-based segmentation model achieves higher mean Intersection-Over-Union than the traditional zero padding on the Cityscapes and the dataset of DeepGlobe satellite  ...  Zero padding is widely used in convolutional neural networks to prevent the size of feature maps diminishing too fast. However, it has been claimed to disturb the statistics at the border.  ...  [18] proposed a distribution padding to maintain the statistics of the border region. Alternatively, Liu et al. [14] proposed a re-weighting based scheme called partial convolution.  ... 
arXiv:2109.07854v1 fatcat:vtbilcessfdizg4f5msw3yazci

Dealiased convolutions for pseudospectral simulations

Malcolm Roberts, John C Bowman
2011 Journal of Physics, Conference Series  
Efficient algorithms have recently been developed for calculating dealiased linear convolution sums without the expense of conventional zero-padding or phase-shift techniques.  ...  For one-dimensional in-place convolutions, the memory requirements are identical with the zero-padding technique, with the important distinction that the additional work memory need not be contiguous with  ...  Implicit zero padding The key idea behind implicitly padding FFT-based convolutions is that the zero-padded input array can be transformed without having to store or process the zero padding [3] .  ... 
doi:10.1088/1742-6596/318/7/072037 fatcat:hlqtfiuoozbalktmzms5w2rwvy

Automatic generation of specialized direct convolutions for mobile GPUs

Naums Mogers, Valentin Radu, Lu Li, Jack Turner, Michael O'Boyle, Christophe Dubach
2020 Proceedings of the 13th Annual Workshop on General Purpose Processing using Graphics Processing Unit  
Using Lift, we show that it is possible to generate automatically code that is ×10 faster than the direct convolution while using ×3.6 less space than the GEMM-based convolution of the very specialized  ...  To reduce effort and reduce time to market, new approaches are needed based on automatic code generation, rather than manual implementation.  ...  The amount of padding ρ is determined automatically by a constraint solver and is explained later. Figure 4 presents an overview of the partial convolution algorithm.  ... 
doi:10.1145/3366428.3380771 dblp:conf/ppopp/MogersRLTOD20 fatcat:342savoeijb3zaznujfmhptoku

Scaling Binarized Neural Networks on Reconfigurable Logic [article]

Nicholas J. Fraser, Yaman Umuroglu, Giulio Gambardella, Michaela Blott, Philip Leong, Magnus Jahre, Kees Vissers
2017 arXiv   pre-print
Finn utilized a novel set of optimizations that enable efficient mapping of BNNs to hardware and implemented fully connected, non-padded convolutional and pooling layers, with per-layer compute resources  ...  Based on this technique, we demonstrate numerous experiments to illustrate flexibility and scalability of the approach.  ...  PADDING FOR BNN CONVOLUTIONS address falls into the padding region, the padding value (e.g.  ... 
arXiv:1701.03400v2 fatcat:lf52l3zre5dxndh6wd2xy3v4h4

EcoFlow: Efficient Convolutional Dataflows for Low-Power Neural Network Accelerators [article]

Lois Orosa, Skanda Koppula, Yaman Umuroglu, Konstantinos Kanellopoulos, Juan Gomez-Luna, Michaela Blott, Kees Vissers, Onur Mutlu
2022 arXiv   pre-print
We find that commonly-used low-power CNN inference accelerators based on spatial architectures are not optimized for both of these convolutional kernels.  ...  Dilated and transposed convolutions introduce significant zero padding when mapped to the underlying spatial architecture, significantly degrading performance and energy efficiency.  ...  The results of these 1D convolutions (or partial sums) are accumulated with other partial sums from other PEs to produce the final ofmap.  ... 
arXiv:2202.02310v1 fatcat:h5qkp4kqi5gaxhfyo7b4zn3dme

Memory-Efficient CNN Accelerator Based on Interlayer Feature Map Compression [article]

Zhuang Shao, Xiaoliang Chen, Li Du, Lei Chen, Yuan Du, Wei Zhuang, Huadong Wei, Chenjia Xie, Zhongfeng Wang
2021 arXiv   pre-print
Existing deep convolutional neural networks (CNNs) generate massive interlayer feature data during network inference.  ...  The on-chip memory allocation scheme is designed to support dynamic configuration of the feature map buffer size and scratch pad size according to different network-layer requirements.  ...  In 3x3 convolution mode, 10 rows and 4 channels partial sums will be sent to the scratch pad each time, where 8 rows are the partial sums of the current RF, and 2 rows are the partial sums of the next  ... 
arXiv:2110.06155v1 fatcat:xxwaszuurnavxdxrhz3cxiebqy

A Unified Hardware Architecture for Convolutions and Deconvolutions in CNN [article]

Lin Bai, Yecheng Lyu, Xinming Huang
2020 arXiv   pre-print
In addition, access to on-chip and off-chip memories is optimized to alleviate the burden introduced by partial sum.  ...  This unified convolution/deconvolution design is applicable to other CNNs with deconvolution.  ...  If a convolution unit is directly reused for deconvolution, it consists of the following two steps: 1) padding the input feature map and 2) applying convolution on the padded feature map, as indicated  ... 
arXiv:2006.00053v1 fatcat:wdtpt5okdjflln7brfufiutvzi

Binarized Encoder-Decoder Network and Binarized Deconvolution Engine for Semantic Segmentation

Hyunwoo Kim, Jeonghoon Kim, Jungwook Choi, Jungkeol Lee, Yong Ho Song.
2020 IEEE Access  
embedded binary activation considering zero-skipped convolution.  ...  The deconvolution used for upsampling in a segmentation network includes zero padding.  ...  Convolution is carried out by partial sum aggregations while propagating the input feature maps that are not zero padded to the PE rows where the weights are stored.  ... 
doi:10.1109/access.2020.3048375 fatcat:kcy6zjhtzzgslmhth367gtephm

Scaling Binarized Neural Networks on Reconfigurable Logic

Nicholas J. Fraser, Yaman Umuroglu, Giulio Gambardella, Michaela Blott, Philip Leong, Magnus Jahre, Kees Vissers
2017 Proceedings of the 8th Workshop and 6th Workshop on Parallel Programming and Run-Time Management Techniques for Many-core Architectures and Design Tools and Architectures for Multicore Embedded Computing Platforms - PARMA-DITAM '17  
Finn utilized a novel set of optimizations that enable efficient mapping of BNNs to hardware and implemented fully connected, non-padded convolutional and pooling layers, with per-layer compute resources  ...  Based on this technique, we demonstrate numerous experiments to illustrate flexibility and scalability of the approach.  ...  PADDING FOR BNN CONVOLUTIONS Padding using nonzero values Zero-padding is commonly applied for convolutional layers in deep neural networks, in order to prevent the pixel information on the image borders  ... 
doi:10.1145/3029580.3029586 dblp:conf/hipeac/FraserUGBLJV17 fatcat:jliterfdmbbp3ao5yly4masuce

Spatiotemporal Estimation of TROPOMI NO2 Column with Depthwise Partial Convolutional Neural Network [article]

Yannic Lops, Masoud Ghahremanloo, Arman Pouyaei, Yunsoo Choi, Jia Jung, Seyedali Mousavinezhad, Ahmed Khan Salman, Davyda Hammond
2022 arXiv   pre-print
This paper expands the application of a partial convolutional neural network (PCNN) to incorporate depthwise convolution layers, conferring temporal dimensionality to the imputation process.  ...  The depthwise convolution process enables the PCNN to independently convolve the data for each channel.  ...  We would like to thank Mathias Gruber for reconstructing the partial CNN code for the TensorFlow implementation, which we have updated and modified.  ... 
arXiv:2204.05917v1 fatcat:f6vmusapgrcwrororaqlsdzpti

Effects of boundary conditions in fully convolutional networks for learning spatio-temporal dynamics [article]

Antonio Alguacil and Wagner Gonçalves Pinto and Michael Bauerheim and Marc C. Jacob and Stéphane Moreau
2021 arXiv   pre-print
In this paper, several strategies to impose boundary conditions (namely padding, improved spatial context, and explicit encoding of physical boundaries) are investigated in the context of fully convolutional  ...  It is then demonstrated that the choice of the optimal padding strategy is directly linked to the data semantics.  ...  Neural Network Convolutional Architecture The auto-regressive strategy can be employed to create surrogate models for physics-based quantities.  ... 
arXiv:2106.11160v3 fatcat:5s5nivwfivfohn3m4ukm3s6b6a

HybridNet: Classification and Reconstruction Cooperation for Semi-supervised Learning [chapter]

Thomas Robert, Nicolas Thome, Matthieu Cord
2018 Lecture Notes in Computer Science  
, 3 × 3, same padding 48 × 48 × 128 Convolution 128 filters, 3 × 3, same padding 48 × 48 × 128 Pooling Maxpool 2 × 2 24 × 24 × 128 Convolution 256 filters, 3 × 3, same padding 24 × 24 × 256 Convolution  ...  Dc and Du Encoders Ec and Eu Inputx 32 × 32 × 3 Convolution 128 filters, 3 × 3, same padding 32 × 32 × 128 Convolution 128 filters, 3 × 3, same padding 32 × 32 × 128 Convolution 128 filters,  ... 
doi:10.1007/978-3-030-01234-2_10 fatcat:c3feqncthrcyzpzhcad6gxpwie

HybridNet: Classification and Reconstruction Cooperation for Semi-Supervised Learning [article]

Thomas Robert, Nicolas Thome, Matthieu Cord
2018 arXiv   pre-print
, 3 × 3, same padding 48 × 48 × 128 Convolution 128 filters, 3 × 3, same padding 48 × 48 × 128 Pooling Maxpool 2 × 2 24 × 24 × 128 Convolution 256 filters, 3 × 3, same padding 24 × 24 × 256 Convolution  ...  Dc and Du Encoders Ec and Eu Inputx 32 × 32 × 3 Convolution 128 filters, 3 × 3, same padding 32 × 32 × 128 Convolution 128 filters, 3 × 3, same padding 32 × 32 × 128 Convolution 128 filters,  ... 
arXiv:1807.11407v1 fatcat:7owzz5nkybe7xcdknefajik2yi

Arbitrary-Scale Image Synthesis [article]

Evangelos Ntavelis, Mohamad Shahbazi, Iason Kastanis, Radu Timofte, Martin Danelljan, Luc Van Gool
2022 arXiv   pre-print
Moreover, we incorporate novel inter-scale augmentations into our pipeline and partial generation training to facilitate the synthesis of consistent images at arbitrary scales.  ...  A recent study called MS-PIE [38] , proposes a padding-free fully-convolutional architecture capable of multi-scale generation based on the input positional encoding and the global latent code.  ...  Using p enc (a) as the input to our StyleGAN2-based architecture the resolution of the intermediate feature maps is: n 0 out = n + 2n pad − 2 For the first convolution n l out = n l−1 out * 2 − 4 For each  ... 
arXiv:2204.02273v1 fatcat:6yxcvw2vhzezfg4xm2tnwqoaza
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