Diffy: a Déjà vu-Free Differential Deep Neural Network Accelerator
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[1] Alessandro Foi,et al. Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering , 2007, IEEE Transactions on Image Processing.
[2] Lei Zhang,et al. Color demosaicking by local directional interpolation and nonlocal adaptive thresholding , 2011, J. Electronic Imaging.
[3] Xiaoou Tang,et al. Compression Artifacts Reduction by a Deep Convolutional Network , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[4] Roberto Cipolla,et al. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[5] Aline Roumy,et al. Low-Complexity Single-Image Super-Resolution based on Nonnegative Neighbor Embedding , 2012, BMVC.
[6] Ali Farhadi,et al. YOLO9000: Better, Faster, Stronger , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[7] Joel Emer,et al. Eyeriss: an Energy-efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks Accessed Terms of Use , 2022 .
[8] Lynn Jenner. Hubble’s High-Definition Panoramic View of the Andromeda Galaxy , 2015 .
[9] Jean-Michel Morel,et al. The Noise Clinic: a Blind Image Denoising Algorithm , 2015, Image Process. Line.
[10] Wangmeng Zuo,et al. Learning Deep CNN Denoiser Prior for Image Restoration , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[11] Christian Ledig,et al. Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[12] Chih-Yuan Yang,et al. Single-Image Super-Resolution: A Benchmark , 2014, ECCV.
[13] Gu-Yeon Wei,et al. Minerva: Enabling Low-Power, Highly-Accurate Deep Neural Network Accelerators , 2016, 2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA).
[14] Daniel Rueckert,et al. Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[15] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[16] Jitendra Malik,et al. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.
[17] Alan C. Bovik,et al. A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms , 2006, IEEE Transactions on Image Processing.
[18] Natalie D. Enright Jerger,et al. Cnvlutin: Ineffectual-Neuron-Free Deep Neural Network Computing , 2016, 2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA).
[19] Alberto Delmas,et al. Dynamic Stripes: Exploiting the Dynamic Precision Requirements of Activation Values in Neural Networks , 2017, ArXiv.
[20] Song Han,et al. EIE: Efficient Inference Engine on Compressed Deep Neural Network , 2016, 2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA).
[21] Lei Zhang,et al. Weighted Nuclear Norm Minimization with Application to Image Denoising , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[22] Felix Heide,et al. IDEAL: Image DEnoising AcceLerator , 2017, 2017 50th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO).
[23] J. E. Krist,et al. Deconvolution of Hubble Space Telescope images using simulated point spread functions , 1992 .
[24] William J. Dally,et al. SCNN: An accelerator for compressed-sparse convolutional neural networks , 2017, 2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA).
[25] Ezzatollah Salari,et al. A neural network‐based nonlinear filter for image enhancement , 2002, Int. J. Imaging Syst. Technol..
[26] Lei Zhang,et al. Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising , 2016, IEEE Transactions on Image Processing.
[27] Natalie D. Enright Jerger,et al. Proteus: Exploiting Numerical Precision Variability in Deep Neural Networks , 2016, ICS.
[28] Lei Zhang,et al. FFDNet: Toward a Fast and Flexible Solution for CNN-Based Image Denoising , 2017, IEEE Transactions on Image Processing.
[29] Alberto Delmas,et al. DPRed: Making Typical Activation Values Matter In Deep Learning Computing , 2018, ArXiv.
[30] Erich Elsen,et al. Deep Speech: Scaling up end-to-end speech recognition , 2014, ArXiv.
[31] Ian D. Reid,et al. RefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[32] Patrick Judd,et al. Stripes: Bit-serial deep neural network computing , 2016, 2016 49th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO).
[33] Trevor Darrell,et al. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[34] Michael J. Black,et al. Fields of Experts: a framework for learning image priors , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[35] Michael Elad,et al. On Single Image Scale-Up Using Sparse-Representations , 2010, Curves and Surfaces.
[36] Bernhard Schölkopf,et al. Learning to Deblur , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[37] Kyoung Mu Lee,et al. Accurate Image Super-Resolution Using Very Deep Convolutional Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[38] Matthew Mattina,et al. Euphrates: Algorithm-SoC Co-Design for Low-Power Mobile Continuous Vision , 2018, 2018 ACM/IEEE 45th Annual International Symposium on Computer Architecture (ISCA).
[39] Xiaoou Tang,et al. Image Super-Resolution Using Deep Convolutional Networks , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[40] Kyoung Mu Lee,et al. Deeply-Recursive Convolutional Network for Image Super-Resolution , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[41] Jan Kotera,et al. Convolutional Neural Networks for Direct Text Deblurring , 2015, BMVC.
[42] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[43] Frédo Durand,et al. Deep joint demosaicking and denoising , 2016, ACM Trans. Graph..
[44] Andreas Moshovos,et al. Bit-Pragmatic Deep Neural Network Computing , 2016, 2017 50th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO).
[45] Luca Benini,et al. CBinfer: Change-Based Inference for Convolutional Neural Networks on Video Data , 2017, ICDSC.
[46] Xuan Yang,et al. A Systematic Approach to Blocking Convolutional Neural Networks , 2016, ArXiv.
[47] Jia Wang,et al. DaDianNao: A Machine-Learning Supercomputer , 2014, 2014 47th Annual IEEE/ACM International Symposium on Microarchitecture.
[48] Tae Hyun Kim,et al. Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).