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Convolutional Neural Networks (CNNs) are currently adopted to solve an ever greater number of problems, ranging from speech recognition to image classification and segmentation.
Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex
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Loop tiling for reconfigurable accelerators
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High Performance Convolutional Neural Networks for Document Processing
Kumar Chellapilla, Sidd Puri, and Patrice Simard · 2006
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Hardware Complexity of Modular Multiplication and Exponentiation
Jean-Pierre David, Kassem Kalach, and Nicolas Tittley · 2007
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A Massively Parallel Coprocessor for Convolutional Neural Networks
Murugan Sankaradas, Venkata Jakkula, Srihari Cadambi, Srimat Chakradhar, Igor Durdanovic, Eric Cosatto, and Hans Peter Graf · 2009
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CNP: An FPGA-based processor for Convolutional Networks
C Farabet, C Poulet, J Y Han, Y LeCun, David R. Tobergte, and Shirley Curtis · 2009
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Roofline: An insightful visual performance model for multicore architectures
Samuel Williams, Andrew Waterman, and David Patterson · 2009
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A Dynamically Configurable Coprocessor for Convolutional Neural Networks
Srimat Chakradhar, Murugan Sankaradas, Venkata Jakkula, and Srihari Cadambi · 2010
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A lightweight dataflow approach for design and implementation of SDR systems
Chung-Ching Shen, William Plishker, Hsiang-Huang Wu, and Shuvra S Bhattacharyya · 2010
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NeuFlow: A runtime reconfigurable dataflow processor for vision
C Farabet, B Martini, B Corda, P Akselrod, E Culurciello, and Y LeCun · 2011
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ImageNet Classification with Deep Convolutional Neural Networks
Alex Krizhevsky, Ilya Sutskever, Hinton Geoffrey E., and Geoffrey E Hinton · 2012
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Minimizing computation in convolutional neural networks
Jason Cong and Bingjun Xiao · 2014
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Computing’s energy problem (and what we can do about it)
Mark Horowitz · 2014
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cuDNN: Efficient Primitives for Deep Learning
Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, Jonathan Cohen, John Tran, Bryan Catanzaro, and Evan Shelhamer · 2014
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Caffe: Convolutional Architecture for Fast Feature Embedding
Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell · 2014
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Training deep neural networks with low precision multiplications
Matthieu Courbariaux, Yoshua Bengio, and Jean-Pierre David · 2014
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A 240 G-ops/s mobile coprocessor for deep neural networks
Vinayak Gokhale, Jonghoon Jin, Aysegul Dundar, Berin Martini, and Eugenio Culurciello · 2014
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Fast and Accurate Computation using Stochastic Circuits
Armin Alaghi and John P Hayes · 2014
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A scalable sparse matrix-vector multiplication kernel for energy-efficient sparse-blas on FPGAs
Richard Dorrance, Fengbo Ren, and Dejan Marković · 2014
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, and others · 2015
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Fast R-CNN
Ross Girshick · 2015
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Fully Convolutional Networks for Semantic Segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
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Accelerating Deep Convolutional Neural Networks Using Specialized Hardware
Kalin Ovtcharov, Olatunji Ruwase, Joo-young Kim, Jeremy Fowers, Karin Strauss, and Eric Chung · 2015
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe and Christian Szegedy · 2015
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Going Deeper with Convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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GPU-Based Deep Learning Inference: A Performance and Power Analysis
Nvidia · 2015
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Optimizing FPGA-based Accelerator Design for Deep Convolutional Neural Networks
Chen Zhang, Peng Li, Guangyu Sun, Yijin Guan, Bingjun Xiao, and Jason Cong · 2015
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Deep Learning with Limited Numerical Precision
Suyog Gupta, Ankur Agrawal, Pritish Narayanan, Kailash Gopalakrishnan, and Pritish Narayanan · 2015
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BinaryConnect: Training Deep Neural Networks with binary weights during propagations
Matthieu Courbariaux, Yoshua Bengio, and Jean-Pierre David · 2015
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Saliency detection by multi-context deep learning
Rui Zhao, Wanli Ouyang, Hongsheng Li, and Xiaogang Wang · 2015
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VLSI Implementation of Deep Neural Network Using Integral Stochastic Computing
Arash Ardakani, Francois Leduc-Primeau, Naoya Onizawa, Takahiro Hanyu, and Warren J. Gross · 2015
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Fast Algorithms for Convolutional Neural Networks
Andrew Lavin and Scott Gray · 2015
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Fixed point optimization of deep convolutional neural networks for object recognition
Sajid Anwar, Kyuyeon Hwang, and Wonyong Sung · 2015
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Sparse Convolutional Neural Networks
Baoyuan Liu, Min Wang, Hassan Foroosh, Marshall Tappen, and Marianna Pensky · 2015
Quantized Convolutional Neural Networks for Mobile Devices
Jiaxiang Wu, Cong Leng, Yuhang Wang, Qinghao Hu, and Jian Cheng · 2016
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Binarized neural networks
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 2016
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Deep Compression - Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
Song Han, Huizi Mao, and William J. Dally · 2016
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Towards end-to-end speech recognition with deep convolutional neural networks
Ying Zhang, Mohammad Pezeshki, Philémon Brakel, Saizheng Zhang, Cesar Laurent Yoshua Bengio, and Aaron Courville · 2017
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Can FPGAs Beat GPUs in Accelerating Next-Generation Deep Neural Networks?
Eriko Nurvitadhi, Suchit Subhaschandra, Guy Boudoukh, Ganesh Venkatesh, Jaewoong Sim, Debbie Marr, Randy Huang, Jason OngGeeHock, Yeong Tat Liew, Krishnan Srivatsan, and Duncan Moss · 2017
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Learning both Weights and Connections for Efficient Neural Network
Song Han, Jeff Pool, John Tran, and William J Dally · 2015
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Learning separable filters
Amos Sironi, Bugra Tekin, Roberto Rigamonti, Vincent Lepetit, and Pascal Fua · 2015
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Going Deeper with Embedded FPGA Platform for Convolutional Neural Network
Jiantao Qiu, Jie Wang, Song Yao, Kaiyuan Guo, Boxun Li, Erjin Zhou, Jincheng Yu, Tianqi Tang, Ningyi Xu, Sen Song, Yu Wang, and Huazhong Yang · 2016
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Deep Learning on FPGAs: Past, Present, and Future
Griffin Lacey, Graham W. Taylor, and Shawki Areibi · 2016
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Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1
Matthieu Courbariaux, Itay Hubara, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 2016
Cited alongside, same era.
Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Intel® Stratix® 10 Variable Precision DSP Blocks User Guide
Intel FPGA · 2017
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Efficient Processing of Deep Neural Networks: A Tutorial and Survey
Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang, and Joel Emer · 2017
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Deep CL: OpenCL library to train deep convolutional neural networks, 2017
Hugh Perkins · 2017
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An OpenCL(TM) Deep Learning Accelerator on Arria 10
Utku Aydonat, Shane O’Connell, Davor Capalija, Andrew C. Ling, and Gordon R. Chiu · 2017
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Frequency Domain Acceleration of Convolutional Neural Networks on CPU-FPGA Shared Memory System
Chi Zhang and Viktor Prasanna · 2017
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Design of an Energy-Efficient Accelerator for Training of Convolutional Neural Networks using Frequency-Domain Computation
Jong Hwan Ko, Burhan Ahmad Mudassar, Taesik Na, and Saibal Mukhopadhyay · 2017
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On How to Design Dataflow FPGA-Based Accelerators for Convolutional Neural Networks
Giuseppe Natale, Marco Bacis, and Marco Domenico Santambrogio · 2017
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Tactics to Directly Map CNN graphs on Embedded FPGAs
Kamel Abdelouahab, Maxime Pelcat, Jocelyn Serot, Cedric Bourrasset, and François Berry · 2017
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PLACID: A Platform for FPGA-Based Accelerator Creation for DCNNs
Mohammad Motamedi, Philipp Gysel, and Soheil Ghiasi · 2017
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Automated Systolic Array Architecture Synthesis for High Throughput CNN Inference on FPGAs
Xuechao Wei, Cody Hao Yu, Peng Zhang, Youxiang Chen, Yuxin Wang, Han Hu, Yun Liang, and Jason Cong · 2017
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Pruning Convolutional Neural Networks for Resource Efficient Learning
Pavlo Molchanov, Stephen Tyree, Tero Karras, Timo Aila, and Jan Kautz · 2017
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An FPGA Realization of a Deep Convolutional Neural Network Using a Threshold Neuron Pruning
Tomoya Fujii, Simpei Sato, Hiroki Nakahara, and Masato Motomura · 2017
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FINN: A Framework for Fast, Scalable Binarized Neural Network Inference
Yaman Umuroglu, Nicholas J Fraser, Giulio Gambardella, Michaela Blott, Philip Leong, Magnus Jahre, and Kees Vissers · 2017
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A New Stochastic Computing Multiplier with Application to Deep Convolutional Neural Networks
Hyeonuk Sim and Jongeun Lee · 2017
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Energy-Efficient Hybrid Stochastic-Binary Neural Networks for Near-Sensor Computing
Vincent T Lee, Armin Alaghi, John P Hayes, Visvesh Sathe, and Luis Ceze · 2017
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SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing
Ao Ren, Ji Li, Zhe Li, Caiwen Ding, Xuehai Qian, Qinru Qiu, Bo Yuan, and Yanzhi Wang · 2017
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Improving the Performance of OpenCL-based FPGA Accelerator for Convolutional Neural Network
Jialiang Zhang and Jing Li · 2017
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Comprehensive Evaluation of OpenCL-based Convolutional Neural Network Accelerators in Xilinx and Altera FPGAs
R. Tapiador, A. Rios-Navarro, A. Linares-Barranco, Minkyu Kim, Deepak Kadetotad, and Jae-sun Seo · 2017
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Evaluating fast algorithms for convolutional neural networks on FPGAs
Liqiang Lu, Yun Liang, Qingcheng Xiao, and Shengen Yan · 2017
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Optimizing Loop Operation and Dataflow in FPGA Acceleration of Deep Convolutional Neural Networks
Yufei Ma, Yu Cao, Sarma Vrudhula, and Jae-sun Seo · 2017
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End-to-end scalable FPGA accelerator for deep residual networks
Yufei Ma, Minkyu Kim, Yu Cao, Sarma Vrudhula, and Jae-sun Seo · 2017
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An automatic RTL compiler for high-throughput FPGA implementation of diverse deep convolutional neural networks
Yufei Ma, Yu Cao, Sarma Vrudhula, and Jae-sun Seo · 2017
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Throughput-Optimized FPGA Accelerator for Deep Convolutional Neural Networks
Zhiqiang Liu, Yong Dou, Jingfei Jiang, Jinwei Xu, Shijie Li, Yongmei Zhou, and Yingnan Xu · 2017
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Latency-Driven Design for FPGA-based Convolutional Neural Networks
Stylianos I Venieris and Christos Savvas Bouganis · 2017
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Balanced Quantization: An Effective and Efficient Approach to Quantized Neural Networks
Shuchang Zhou, Yuzhi Wang, He Wen, Qinyao He, and Yuheng Zou · 2017
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FixCaffe: Training CNN with Low Precision Arithmetic Operations by Fixed Point Caffe
Shasha Guo, Lei Wang, Baozi Chen, Qiang Dou, Yuxing Tang, and Zhisheng Li · 2017
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A fully connected layer elimination for a binarizec convolutional neural network on an FPGA
Hiroki Nakahara, Tomoya Fujii, and Shimpei Sato · 2017
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Trained Ternary Quantization
Chenzhuo Zhu, Song Han, Huizi Mao, and William J. Dally · 2017
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Accelerating Binarized Convolutional Neural Networks with Software-Programmable FPGAs
Ritchie Zhao, Weinan Song, Wentao Zhang, Tianwei Xing, Jeng-Hau Lin, Mani Srivastava, Rajesh Gupta, and Zhiru Zhang · 2017
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Scaling Binarized Neural Networks on Reconfigurable Logic
Nicholas J Fraser, Yaman Umuroglu, Giulio Gambardella, Michaela Blott, Philip Leong, Magnus Jahre, and Kees Vissers · 2017
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FP-BNN: Binarized Neural Network on FPGA
Shuang Liang, Shouyi Yin, Leibo Liu, Wayne Luk, and Shaojun Wei · 2017
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Scalable High-Performance Architecture for Convolutional Ternary Neural Networks on FPGA
Adrien ProstBoucle, Alban Bourge, Frédéric Pétrot, Hande Alemdar, Nicholas Caldwell, and Vincent Leroy · 2017
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Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning
Tien-Ju Yang, Yu-Hsin Chen, and Vivienne Sze · 2017
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