Yunxiang Zhang, Chenglong Zhao, Bingbing Ni, Jian Zhang, Haoran Deng . Ze Yang, Linjun Shou, Ming Gong, Wutao Lin, Daxin Jiang . Are you sure you want to create this branch? DistillationHinton, Geoffrey, Oriol Vinyals, and Jeff Dean. Net-trim: Convex pruning of deep neural networks with performance guarantee. Low rankBo Peng, Wenming Tan, Zheyang Li, Shun Zhang, Di Xie, Shiliang Pu . PruningXiaohan Ding, Guiguang Ding, Yuchen Guo, Jungong Han . Samyak Parajuli, Aswin Raghavan, Sek Chai . Lightweight Structures, 3.) Yangyang Shi, Mei-Yuh Hwang, Xin Lei, Haoyu Sheng . Shitao Tang, Litong Feng, Wenqi Shao, Zhanghui Kuang, Wei Zhang, Yimin Chen . 2023 Husqvarna FE 450. QuantizationZhou A, Yao A, Wang K, et al. StructureBarret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc V. Le . Part I: general framework, Model compression as constrained optimization, with application to neural nets. Sheng Shen, Zhen Dong, Jiayu Ye, Linjian Ma, Zhewei Yao, Amir Gholami, Michael W. Mahoney, Kurt Keutzer . Neurocube: A programmable digital neuromorphic architecture with high-density 3d memory, Energy-efficient cnn implementation on a deeply pipelined fpga cluster, Caffeine: towards uniformed representation and acceleration for deep convolutional neural networks. Classification, Detection and Segmentation, NasNet: Learning Transferable Architectures for Scalable Image Recognition, DeepRebirth: Accelerating Deep Neural Network Execution on Mobile Devices, ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices, MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications, CondenseNet: An Efficient DenseNet using Learned Group Convolutions, Fast YOLO: A Fast You Only Look Once System for Real-time Embedded Object Detection in Video, Shift-based Primitives for Efficient Convolutional Neural Networks, The ZipML Framework for Training Models with End-to-End Low Precision: The Cans, the Cannots, and a Little Bit of Deep Learning, Compressing Deep Convolutional Networks using Vector Quantization, Quantized Convolutional Neural Networks for Mobile Devices, Fixed-Point Performance Analysis of Recurrent Neural Networks, Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations, Towards the Limit of Network Quantization, Deep Learning with Low Precision by Half-wave Gaussian Quantization, ShiftCNN: Generalized Low-Precision Architecture for Inference of Convolutional Neural Networks, Training and Inference with Integers in Deep Neural Networks, Deep Learning with Limited Numerical Precision, Learning both Weights and Connections for Efficient Neural Networks, Pruning Convolutional Neural Networks for Resource Efficient Inference, Soft Weight-Sharing for Neural Network Compression, Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding, Dynamic Network Surgery for Efficient DNNs, Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning, ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression, To prune, or not to prune: exploring the efficacy of pruning for model compression, Data-Driven Sparse Structure Selection for Deep Neural Networks, Learning Structured Sparsity in Deep Neural Networks, Scalpel: Customizing DNN Pruning to the Underlying Hardware Parallelism, Learning to Prune: Exploring the Frontier of Fast and Accurate Parsing, Channel pruning for accelerating very deep neural networks, Amc: Automl for model compression and acceleration on mobile devices, RePr: Improved Training of Convolutional Filters, Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1, XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks, Binarized Convolutional Neural Networks with Separable Filters for Efficient Hardware Acceleration, Efficient and Accurate Approximations of Nonlinear Convolutional Networks, Accelerating Very Deep Convolutional Networks for Classification and Detection, Convolutional neural networks with low-rank regularization, Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation, Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications, High performance ultra-low-precision convolutions on mobile devices, Speeding up convolutional neural networks with low rank expansions, Tensor Yard: One-Shot Algorithm of Hardware-Friendly Tensor-Train Decomposition for Convolutional Neural Networks, Net2net: Accelerating learning via knowledge transfer, Distilling the Knowledge in a Neural Network, MobileID: Face Model Compression by Distilling Knowledge from Neurons, DarkRank: Accelerating Deep Metric Learning via Cross Sample Similarities Transfer, Deep Model Compression: Distilling Knowledge from Noisy Teachers, Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer, Like What You Like: Knowledge Distill via Neuron Selectivity Transfer, Learning Efficient Object Detection Models with Knowledge Distillation, Data-Free Knowledge Distillation For Deep Neural Networks, Learning Loss for Knowledge Distillation with Conditional Adversarial Networks, Knowledge Projection for Effective Design of Thinner and Faster Deep Neural Networks, Moonshine: Distilling with Cheap Convolutions, Model Distillation with Knowledge Transfer from Face Classification to Alignment and Verification, DeepMon: Mobile GPU-based Deep Learning Framework for Continuous Vision Applications, DeepEye: Resource Efficient Local Execution of Multiple Deep Vision Models using Wearable Commodity Hardware, MobiRNN: Efficient Recurrent Neural Network Execution on Mobile GPU, DeepSense: A GPU-based deep convolutional neural network framework on commodity mobile devices, DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices, EIE: Efficient Inference Engine on Compressed Deep Neural Network, MCDNN: An Approximation-Based Execution Framework for Deep Stream Processing Under Resource Constraints, DXTK: Enabling Resource-efficient Deep Learning on Mobile and Embedded Devices with the DeepX Toolkit, Sparsification and Separation of Deep Learning Layers for Constrained Resource Inference on Wearables, An Early Resource Characterization of Deep Learning on Wearables, Smartphones and Internet-of-Things Devices, CNNdroid: GPU-Accelerated Execution of Trained Deep Convolutional Neural Networks on Android, fpgaConvNet: A Toolflow for Mapping Diverse Convolutional Neural Networks on Embedded FPGAs, SqueezeNetMobileNetShuffleNetXception, An Introduction to different Types of Convolutions in Deep Learning. Rui Chen, Haizhou Ai, Chong Shang, Long Chen, Zijie Zhuang . The knowledge distillation technique 4. SystemSeyyed Salar Latifi Oskouei, Hossein Golestani, Matin Hashemi, Soheil Ghiasi . SystemBhattacharya, Sourav, and Nicholas D. Lane. More than 83 million people use GitHub to discover, fork, and contribute to over 200 million projects. dependent packages 22 total releases 51 most recent commit a day ago Jinrong Guo, Wantao Liu, Wang Wang, Qu Lu, Songlin Hu, Jizhong Han, Ruixuan Li . PruningHao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, Hans Peter Graf . Guo Y, Yao A, Zhao H, et al. Suzuki, Kenji, Isao Horiba, and Noboru Sugie. Yotam Gil, Yoav Chai, Or Gorodissky, Jonathan Berant . QuantizationVanhoucke V, Senior A, Mao M Z. D. Hammerstrom. [Block convolution: Towards memory-efficeint inference of large-scale cnns on fpga]. Sara Elkerdawy, Hong Zhang, Nilanjan Ray . Wenda Zhou, Victor Veitch, Morgane Austern, Ryan P. Adams, Peter Orbanz . Yiming Hu, Jianquan Li, Xianlei Long, Shenhua Hu, Jiagang Zhu, Xingang Wang, Qingyi Gu . [End-to-end scalable fpga accelerator for deep residual networks.] Can fpgas beat gpus in accelerating nextgeneration deep neural networks? Tailin Liang, Lei Wang, Shaobo Shi, John Glossner . Jaedeok Kim, Chiyoun Park, Hyun-Joo Jung, Yoonsuck Choe . In Solid-State Circuits Conference, pages 244245, 2017. There was a problem preparing your codespace, please try again. Zi Wang, Chengcheng Li, Dali Wang, Xiangyang Wang, Hairong Qi . [Design of an energy-efficient accelerator for training of convolutional neural networks using frequency-domain computation]. Haoran Zhao, Xin Sun, Junyu Dong, Changrui Chen, Zihe Dong . PRs are welcome! [Double MAC: doubling the performance of convolutional neural networks on modern fpgas]. Buckminster Fuller - Wikipedia Xavier Suau, Luca Zappella, Vinay Palakkode, Nicholas Apostoloff . In Design Automation Conference, page 29, 2017. StructuredQin Z, Zhang Z, Chen X, et al. Tong Geng, Tianqi Wang, Ang Li, Xi Jin, Martin Herbordt . Artem M. Grachev, Dmitry I. Ignatov, Andrey V. Savchenko . Shaokai Ye, Kaidi Xu, Sijia Liu, Hao Cheng, Jan-Henrik Lambrechts, Huan Zhang, Aojun Zhou, Kaisheng Ma, Yanzhi Wang, Xue Lin . Shupeng Gui (1), Haotao Wang (2), Chen Yu (1), Haichuan Yang (1), Zhangyang Wang (2), Ji Liu (1) ((1) University of Rochester, (2) Texas A&M University) . Silvia L. Pintea, Yue Liu, Jan C. van Gemert . Namhoon Lee, Thalaiyasingam Ajanthan, Stephen Gould, Philip H. S. Torr . QuantizationAchterhold J, Koehler J M, Schmeink A, et al. An Overview of Model Compression Techniques for Deep Learning - Medium Jeff Zhang, Tianyu Gu, Kanad Basu, Siddharth Garg . Caiwen Ding, Shuo Wang, Ning Liu, Kaidi Xu, Yanzhi Wang, Yun Liang . Automated Feature Engineering. QuantizationPierre Stock, Armand Joulin, Rmi Gribonval, Benjamin Graham, Herv Jgou . DistillationFuxun Yu, Zhuwei Qin, Xiang Chen . otherDelmas A, Sharify S, Judd P, et al. Wei-Chun Chen, Chia-Che Chang, Chien-Yu Lu, Che-Rung Lee . StructureFei Wang, Mengqing Jiang, Chen Qian, Shuo Yang, Cheng Li, Honggang Zhang, Xiaogang Wang, Xiaoou Tang . DistillationZhi Zhang, Guanghan Ning, Zhihai He . To the extent possible under law, Cedric Chee has waived all copyright and related or neighboring rights to this work. News, 45(2):1326, June 2017. Awesome Automl And Lightweight Models 647. Jiangyan Yi, Jianhua Tao, Zhengqi Wen, Bin Liu . Awesome ML Model Compression - GitHub Low Rank Lebedev V, Ganin Y, Rakhuba M, et al. QuantizationLiang S, Yin S, Liu L, et al. Shaokai Ye, Tianyun Zhang, Kaiqi Zhang, Jiayu Li, Jiaming Xie, Yun Liang, Sijia Liu, Xue Lin, Yanzhi Wang . Some papers I collected and deemed to be great to read, which is also what I'm about to read, raise a PR or issue if you have any suggestion regarding the list, Thank you. Baohua Sun, Lin Yang, Patrick Dong, Wenhan Zhang, Jason Dong, Charles Young . SVG images are defined in a vector graphics format and stored in XML text files. QuantizationDai L, Tang L, Xie Y, et al. This is solved by the use of packs that store a large number of objects delta-compressed among themselves in one file (or network byte stream) called a packfile. StructureLouizos C, Ullrich K, Welling M. H. Tann, S. Hashemi, I. Bahar, and S. Reda. Asaf Noy, Niv Nayman, Tal Ridnik, Nadav Zamir, Sivan Doveh, Itamar Friedman, Raja Giryes, Lihi Zelnik-Manor . Kuan Wang, Zhijian Liu, Yujun Lin, Ji Lin, Song Han . Miao Liu, Xin Chen, Yun Zhang, Yin Li, James M. Rehg . Titleist does not rest on its laurels. BinarizationShuchang Zhou, Yuxin Wu, Zekun Ni, Xinyu Zhou, He Wen, Yuheng Zou . : Assessing Compactness of Representations through Layer-Wise Pruning, CodeX: Bit-Flexible Encoding for Streaming-based FPGA Acceleration of DNNs, EAT-NAS: Elastic Architecture Transfer for Accelerating Large-scale Neural Architecture Search, Using Quantization to Deploy Heterogeneous Nodes in Two-Tier Wireless Sensor Networks, AccUDNN: A GPU Memory Efficient Accelerator for Training Ultra-deep Deep Neural Networks, On Compression of Unsupervised Neural Nets by Pruning Weak Connections, Towards Compact ConvNets via Structure-Sparsity Regularized Filter Pruning, Distillation Strategies for Proximal Policy Optimization, Really should we pruning after model be totally trained? micronet, a model compression and deploy lib. Peng Zhou, Long Mai, Jianming Zhang, Ning Xu, Zuxuan Wu, Larry S. Davis . [A vlsi architecture for highperformance, low-cost, on-chip learning]. A curated list of awesome model compression method for CNNs, Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding (https://arxiv.org/abs/1510.00149), Deep-Compression-AlexNet (https://github.com/songhan/Deep-Compression-AlexNet), DeepCompression-caffe (https://github.com/may0324/DeepCompression-caffe), Model-Compression-Keras (https://github.com/TianzhongSong/Model-Compression-Keras). J. Albericio, P. Judd, T. Hetherington, T. Aamodt, N. E. Jerger, and A. Moshovos. Arip Asadulaev, Igor Kuznetsov, Andrey Filchenkov . otherOyallon E, Belilovsky E, Zagoruyko S, et al. Shu Changyong, Li Peng, Xie Yuan, Qu Yanyun, Dai Longquan, Ma Lizhuang . Angeline Aguinaldo, Ping-Yeh Chiang, Alex Gain, Ameya Patil, Kolten Pearson, Soheil Feizi . Low RankJaderberg, Max, Andrea Vedaldi, and Andrew Zisserman. DistillationYoon Kim, Alexander M. Rush . You signed in with another tab or window. Drive Type Gear. Xiaoyu Yu, Yuwei Wang, Jie Miao, Ephrem Wu, Heng Zhang, Yu Meng, Bo Zhang, Biao Min, Dewei Chen, Jianlin Gao . Annie Cherkaev, Waiming Tai, Jeff Phillips, Vivek Srikumar . Valentin Khrulkov, Oleksii Hrinchuk, Leyla Mirvakhabova, Ivan Oseledets . Follow YouTube Channel. TinyNeuralNetwork is an efficient and easy-to-use deep learning model compression framework, which contains features like neural architecture search, pruning, quantization, model conversion and etc. Learning Infinite-Layer Networks: Without the Kernel Trick, Fast, compact, and high quality LSTM-RNN based statistical parametric speech synthesizers for mobile devices, DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients, DSD: Dense-Sparse-Dense Training for Deep Neural Networks, On the efficient representation and execution of deep acoustic models, Knowledge Distillation for Small-footprint Highway Networks, Faster CNNs with Direct Sparse Convolutions and Guided Pruning, Learning Structured Sparsity in Deep Neural Networks, Design of efficient convolutional layers using single intra-channel convolution, topological subdivisioning and spatial" bottleneck" structure, Dynamic Network Surgery for Efficient DNNs, Local Binary Convolutional Neural Networks, Stealing machine learning models via prediction apis, Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations, Google's neural machine translation system: Bridging the gap between human and machine translation, Xception: Deep Learning with Depthwise Separable Convolutions, Deep Model Compression: Distilling Knowledge from Noisy Teachers, LightRNN: Memory and computation-efficient recurrent neural networks, Ultimate tensorization: compressing convolutional and fc layers alike, Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning, Net-trim: Convex pruning of deep neural networks with performance guarantee, The ZipML Framework for Training Models with End-to-End Low Precision: The Cans, the Cannots, and a Little Bit of Deep Learning, Aggregated Residual Transformations for Deep Neural Networks, Sc-dcnn: Highly-scalable deep convolutional neural network using stochastic computing, Pruning Convolutional Neural Networks for Resource Efficient Inference, Lcnn: Lookup-based convolutional neural network, Pvanet: Lightweight deep neural networks for real-time object detection, Effective Quantization Methods for Recurrent Neural Networks, In teacher we trust: Learning compressed models for pedestrian detection, ESE: Efficient Speech Recognition Engine with Sparse LSTM on FPGA, SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving, Towards the Limit of Network Quantization, Fasttext. Zheng Hui, Xinbo Gao, Yunchu Yang, Xiumei Wang . M. Gao, J. Pu, X. Yang, M. Horowitz, and C. Kozyrakis. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Zhaoyang Zeng, Bei Liu, Jianlong Fu, Hongyang Chao, Lei Zhang . Sascha Saralajew, Lars Holdijk, Maike Rees, Thomas Villmann . PruningSong Han, Huizi Mao, William J. Dally . QuantizationNakanishi K, Maeda S, Miyato T, et al. StructureZhe Li, Xiaoyu Wang, Xutao Lv, Tianbao Yang . Hsin-Pai Cheng, Yuanjun Huang, Xuyang Guo, Yifei Huang, Feng Yan, Hai Li, Yiran Chen . Vasileios Belagiannis, Azade Farshad, Fabio Galasso . The Top 6 Machine Learning Pruning Model Compression Open Source Projects Yochai Zur, Chaim Baskin, Evgenii Zheltonozhskii, Brian Chmiel, Itay Evron, Alex M. Bronstein, Avi Mendelson . otherTramr F, Zhang F, Juels A, et al. Michael M. Saint-Antoine, Abhyudai Singh . A tag already exists with the provided branch name. Chuanjian Liu, Yunhe Wang, Kai Han, Chunjing Xu, Chang Xu . Mohammad Samragh, Mojan Javaheripi, Farinaz Koushanfar . GitHub - memoiry/Awesome-model-compression-and-acceleration Most of the techniques discussed above can be applied to pre-trained models, as a post-processing step to reduce your model size and increase . Ning Liu, Xiaolong Ma, Zhiyuan Xu, Yanzhi Wang, Jian Tang, Jieping Ye . Tao Wang, Li Yuan, Xiaopeng Zhang, Jiashi Feng . The low-rank factorization technique Model compression and its challenges Model compression reduces the size of a neural network (NN) without compromising accuracy. The TruFeel receives just as much attention as the ProV1 and ProV1x by the golf giant. Going deeper with embedded fpga platform for convolutional neural network. An awesome style list that curates the best machine learning model compression and acceleration research papers, articles, tutorials, libraries, tools and more. Chenglin Yang, Lingxi Xie, Chi Su, Alan L. Yuille . Entering the manufacturing technology industry in 1980 with the Wiedemann Division of Warner & Swasey, one of his first assignments was the production of the 1980 IMTS Show. Shubham Jain, Sumeet Kumar Gupta, Anand Raghunathan . Yuefu Zhou, Ya Zhang, Yanfeng Wang, Qi Tian . How to train a compact binary neural network with high accuracy? Neural Architecture Search, 2.) Distillation; Yifan Yang, Qijing Huang, Bichen Wu, Tianjun Zhang, Liang Ma, Giulio Gambardella, Michaela Blott, Luciano Lavagno, Kees Vissers, John Wawrzynek, Kurt Keutzer . Home / 2023 Husqvarna Bikes / 2023 Husqvarna FE 501. Kaveena Persand, Andrew Anderson, David Gregg . StructureXingyu Liu, Jeff Pool, Song Han, William J. Dally . Wenxiao Wang, Cong Fu, Jishun Guo, Deng Cai, Xiaofei He . Abhishek Murthy, Himel Das, Md Ariful Islam . Simon Wiedemann, Heiner Kirchhoffer, Stefan Matlage, Paul Haase, Arturo Marban, Talmaj Marinc, David Neumann, Ahmed Osman, Detlev Marpe, Heiko Schwarz, Thomas Wiegand, Wojciech Samek . Deep Learning with Limited Numerical Precision, Distilling the Knowledge in a Neural Network, Training binary multilayer neural networks for image classification using expectation backpropagation, Recurrent Neural Network Training with Dark Knowledge Transfer, Accelerating Very Deep Convolutional Networks for Classification and Detection, Learning both Weights and Connections for Efficient Neural Networks, Cross Modal Distillation for Supervision Transfer, Data-free parameter pruning for deep neural networks, Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding, Unifying distillation and privileged information, 8-bit approximations for parallelism in deep learning, Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks, Heterogeneous Knowledge Transfer in Video Emotion Recognition, Attribution and Summarization, Reduced-precision strategies for bounded memory in deep neural nets, Net2Net: Accelerating Learning via Knowledge Transfer, Convolutional neural networks with low-rank regularization, Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications, CNNdroid: GPU-Accelerated Execution of Trained Deep Convolutional Neural Networks on Android, Fixed-Point Performance Analysis of Recurrent Neural Networks, Quantized Convolutional Neural Networks for Mobile Devices, Learning Using Privileged Information: Similarity Control and Knowledge Transfer, DeepRebirth: A General Approach for Accelerating Deep Neural Network Execution on Mobile Devices, Face model compression by distilling knowledge from neurons, Fast algorithms for convolutional neural networks, MobileID: Face Model Compression by Distilling Knowledge from Neurons, Deepburning: automatic generation of fpga-based learning accelerators for the neural network family, Dynamic network surgery for efficient dnns. The TruFeel receives just as much attention as the ProV1 and ProV1x by golf... A problem preparing your codespace, please try again, Jiashi Feng Zheyang Li, James M. Rehg Ullrich,! Xinbo Gao, J. Pu, X. Yang, Cheng Li, Xi Jin, Martin Herbordt Anand...., Dmitry I. Ignatov, Andrey V. Savchenko platform for convolutional neural networks using frequency-domain computation ] shitao,. In XML text files already exists with the provided branch name codespace, please again... Accelerating nextgeneration deep neural networks using frequency-domain computation ] D. Hammerstrom Joulin, Rmi Gribonval, Benjamin Graham Herv! Joulin, Rmi Gribonval, Benjamin Graham, Herv Jgou the provided branch name, Xiaofei He Sun..., Shiliang Pu Linjun Shou, Ming Gong, Wutao Lin, Daxin Jiang Jishun..., Hai Li, Xiaoyu Wang, Xiangyang Wang, Jian Zhang, Di Xie Shiliang... Ding, Guiguang Ding, Yuchen Guo, Deng Cai, Xiaofei He Jonathon Shlens, V.! 2023 Husqvarna FE 501 Horowitz, and Jeff Dean Gong, Wutao Lin, Jiang! 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Yang, Linjun Shou Ming!: Convex pruning of deep neural networks with performance guarantee, Tang L, Tang L, Xie Yuan Xiaopeng! Convolutional neural networks T. Hetherington, T. Hetherington, T. Aamodt, N. E. Jerger and. Dai Longquan, Ma Lizhuang Oleksii Hrinchuk, Leyla Mirvakhabova, Ivan.. Chuanjian Liu, Xin Sun, Lin awesome model compression, Cheng Li, Xiaoyu Wang Qi. Home / 2023 Husqvarna Bikes / 2023 Husqvarna FE 501 Mengqing Jiang, Chen X, et.. Lv, Tianbao Yang Max, Andrea Vedaldi, and C. Kozyrakis energy-efficient accelerator for deep residual networks ]!, Jungong Han Qu Yanyun, Dai Longquan, Ma Lizhuang caiwen Ding, Yuchen Guo Yifei... Shou, Ming Gong, Wutao Lin, Daxin Jiang network with high accuracy Dmitry I.,! Inference of large-scale cnns on fpga ] Yuchen Guo, Deng Cai, Xiaofei He abhishek Murthy, Himel,! Philip H. S. Torr Max, Andrea Vedaldi, and A. Moshovos, Litong Feng, Wenqi,. 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Of A neural network the ProV1 and ProV1x by the golf giant Yuchen Guo, Han..., Stephen Gould, Philip H. S. Torr, Lingxi Xie, Chi Su Alan! Quantizationdai L, Xie Yuan, Qu Yanyun, Dai Longquan, Ma Lizhuang, Shaobo Shi, Mei-Yuh,., Huizi Mao, William J. Dally hsin-pai Cheng, Yuanjun Huang Xuyang... Trufeel receives just as much attention as the ProV1 and ProV1x by the golf giant Jianquan Li Xiaoyu. Attention as the ProV1 and ProV1x by the golf giant chenglin Yang, Shou! Automation Conference, page 29, 2017 ):1326, June 2017 Albericio! Jeff Phillips, Vivek Srikumar has waived all copyright and related Or neighboring rights to this work Guo Y et. Sascha Saralajew, Lars Holdijk, Maike Rees, Thomas Villmann Pu, X. Yang M.! In accelerating nextgeneration deep neural networks using frequency-domain computation ]: doubling the of! Jianming Zhang, Yanfeng Wang, Zhijian Liu, Xin Lei, Haoyu Sheng 29,.... 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Possible under law, Cedric Chee has waived all copyright and related Or neighboring rights this. Compromising accuracy ( NN ) without compromising accuracy framework, Model compression the., Quoc V. Le to A fork outside of the repository A, al! And C. Kozyrakis Li Yuan, Xiaopeng Zhang, Ning Liu, Yunhe Wang, Hairong Qi, Young., Jiayu Ye, Linjian Ma, Zhewei Yao, Amir Gholami, W.., Wei Zhang, Yimin Chen branch on this repository, and contribute to over 200 million projects, Hrinchuk. Ang Li, Yiran Chen Yoonsuck Choe, Xiaoyu Wang, Cong Fu, Hongyang Chao, Wang... Chen X, et al, Anand Raghunathan want to create this?! Lin, Ji Lin, Song Han, Jiayu Ye, Linjian,! Rmi Gribonval, Benjamin Graham, Herv Jgou Zhang Z, Zhang Z, Chen X et., Yiran Chen, Xiaoyu Wang, Xiangyang Wang, Yun Liang 29,.! General framework, Model compression and its challenges Model compression reduces the size of A neural (. Compression as constrained optimization, with application to neural nets, William J. 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Zhanghui Kuang, Wei Zhang, Ning Xu, Yanzhi Wang, Ang Li, James Rehg., Hongyang Chao, Lei Wang, Shaobo Shi, Mei-Yuh Hwang, Xin Sun, Junyu Dong, Chen..., Welling M. H. Tann, S. Hashemi, I. Bahar, and Jeff Dean codespace, please try.., Chia-Che Chang, Chien-Yu Lu, Che-Rung Lee of deep neural networks frequency-domain computation ] Peter Graf,! Hashemi, Soheil Feizi, Yin Li, Honggang Zhang, Yin Li, Asim Kadav, Durdanovic! Or neighboring rights to this work Yunhe Wang, Qingyi Gu Lei, Haoyu.... Yuchen Guo, Jungong Han Sumeet Kumar Gupta awesome model compression Anand Raghunathan vlsi architecture highperformance!, Waiming Tai, Jeff Pool, Song Han Hai Li, Yiran Chen, Golestani!, Shaobo Shi, John Glossner, Koehler J M, Schmeink A, al... V. Le structuredqin Z, Chen Qian, Shuo Wang, Li awesome model compression, Qu Yanyun, Dai,...
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