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Convolutional neural networks (CNN) have achieved major breakthroughs in recent years.
Multilayer Feedforward Neural Networks with Single Powers-of-Two Weights
Tang, C. Z. and Kwan, H. K · 1993
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Gradient-Based Learning Applied to Document Recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
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Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G · 2004
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Histograms of oriented gradients for human detection
Dalal, N. and Triggs, B · 2005
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A backpropagation neural network design using adder-only arithmetic
Mahoney, V. and Elhanany, I · 2008
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Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A · 2009
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Rectified Linear Units Improve Restricted Boltzmann Machines
Nair, V. and Hinton, G. E · 2010
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Random Search for Hyper-Parameter Optimization
Bergstra, J. and Bengio, Y · 2012
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Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups
Hinton, G., Deng, L., Yu, D., Dahl, G. E., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T. N., et al · 2012
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ImageNet Classification with Deep Convolutional Neural Networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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Advances in Optimizing Recurrent Networks
Bengio, Y., Boulanger-Lewandowski, N., and Pascanu, R · 2013
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Fast Training of Convolutional Networks through FFTs
Mathieu, M., Henaff, M., and LeCun, Y · 2013
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DaDianNao: A Machine-Learning Supercomputer
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cuDNN: Efficient Primitives for Deep Learning
Chetlur, S., Woolley, C., Vandermersch, P., Cohen, J., Tran, J., Catanzaro, B., and Shelhamer, E · 2014
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Training Deep Neural Networks with Low Precision Multiplications
Courbariaux, M., David, J.-P., and Bengio, Y · 2014
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Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation
Denton, E. L., Zaremba, W., Bruna, J., LeCun, Y., and Fergus, R · 2014
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Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., and Darrell, T · 2014
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TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, 2015
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X · 2015
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BinaryConnect: Training Deep Neural Networks with binary weights during propagations
Courbariaux, M., Bengio, Y., and David, J.-P · 2015
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Leveraging the Error Resilience of Neural Networks for Designing Highly Energy Efficient Accelerators
Du, Z., Lingamneni, A., Chen, Y., Palem, K. V., Temam, O., and Wu, C · 2015
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Optimizing FPGA-based Accelerator Design for Deep Convolutional Neural Networks
Zhang, C., Li, P., Sun, G., Guan, Y., Xiao, B., and Cong, J · 2015
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Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks
Chen, Y.-H., Krishna, T., Emer, J., and Sze, V · 2016
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Reduced-Precision Memory Value Approximation for Deep Learning
Deng, Z., Xu, C., Cai, Q., and Faraboschi, P · 2016
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Hardware-oriented Approximation of Convolutional Neural Networks
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Gatys, L. A., Ecker, A. S., and Bethge, M · 2015
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Fast R-CNN
Girshick, R · 2015
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Deep Learning with Limited Numerical Precision
Gupta, S., Agrawal, A., Gopalakrishnan, K., and Narayanan, P · 2015
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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2015
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S. and Szegedy, C · 2015
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Adam: A Method for Stochastic Optimization
Kingma, D. and Ba, J · 2015
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Fixed Point Quantization of Deep Convolutional Networks
Lin, D., Talathi, S., and Annapureddy, S · 2015
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ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L · 2015
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SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
Iandola, F. N., Moskewicz, M. W., Ashraf, K., Han, S., Dally, W. J., and Keutzer, K · 2016
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Stanford CS231n course: Convolutional Neural Networks for Visual Recognition
Karpathy, A · 2016
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PLACID: A Platform for Accelerator Creation for DCNNs
Motamedi, M., Gysel, P., and Ghiasi, S · 2016
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Going Deeper with Embedded FPGA Platform for Convolutional Neural Network
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XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks
Rastegari, M., Ordonez, V., Redmon, J., and Farhadi, A · 2016
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Mastering the game of Go with deep neural networks and tree search
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Sim, J., Park, J.-S., Kim, M., Bae, D., Choi, Y., and Kim, L.-S · 2016
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Throughput-Optimized OpenCL-based FPGA Accelerator for Large-Scale Convolutional Neural Networks
Suda, N., Chandra, V., Dasika, G., Mohanty, A., Ma, Y., Vrudhula, S., Seo, J.-s., and Cao, Y · 2016
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Theano: A Python framework for fast computation of mathematical expressions
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