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Deep neural networks have evolved remarkably over the past few years and they are currently the fundamental tools of many intelligent systems.
Optimal brain damage
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Imagenet classification with deep convolutional neural networks
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Predicting parameters in deep learning
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Dadiannao: A machine-learning supercomputer
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Compressing deep convolutional networks using vector quantization
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1.1 computing’s energy problem (and what we can do about it)
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Fixed-point feedforward deep neural network design using weights+ 1, 0, and- 1
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Speeding up convolutional neural networks with low rank expansions
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Speeding-up convolutional neural networks using fine-tuned cp-decomposition
V. Lebedev, Y. Ganin, M. Rakhuba, I. Oseledets, and V. Lempitsky · 2014
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Fitnets: Hints for thin deep nets
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Very deep convolutional networks for large-scale image recognition
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Z. Cheng, D. Soudry, Z. Mao, and Z. Lan · 2015
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Binaryconnect: Training deep neural networks with binary weights during propagations
M. Courbariaux, Y. Bengio, and J.-P. David · 2015
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8-bit approximations for parallelism in deep learning
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Deep learning with limited numerical precision
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Learning both weights and connections for efficient neural network
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Distilling the knowledge in a neural network
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Compression of deep convolutional neural networks for fast and low power mobile applications
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Neural networks with few multiplications
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Going deeper with convolutions
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Optimizing fpga-based accelerator design for deep convolutional neural networks
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Accelerating very deep convolutional networks for classification and detection
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Cnvlutin: Ineffectual-neuron-free deep neural network computing
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Fused-layer cnn accelerators
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Dynamic network surgery for efficient dnns
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Eie: Efficient inference engine on compressed deep neural network
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Deep residual learning for image recognition
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Loss-aware binarization of deep networks
L. Hou, Q. Yao, and J. T. Kwok · 2016
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Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 0.5 mb model size
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Neurocube: A programmable digital neuromorphic architecture with high-density 3d memory
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Dynamic energy-accuracy trade-off using stochastic computing in deep neural networks
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Ese: Efficient speech recognition engine with sparse lstm on fpga
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Channel pruning for accelerating very deep neural networks
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam · 2017
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In-datacenter performance analysis of a tensor processing unit
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Design of an energy-efficient accelerator for training of convolutional neural networks using frequency-domain computation
J. H. Ko, B. Mudassar, T. Na, and S. Mukhopadhyay · 2017
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M. Kim and P. Smaragdis · 2016
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Fast convnets using group-wise brain damage
V. Lebedev and V. Lempitsky · 2016
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F. Li, B. Zhang, and B. Liu · 2016
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Cambricon: An instruction set architecture for neural networks
S. Liu, Z. Du, J. Tao, D. Han, T. Luo, Y. Xie, Y. Chen, and T. Chen · 2016
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Convolutional neural networks using logarithmic data representation
D. Miyashita, E. H. Lee, and B. Murmann · 2016
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Going deeper with embedded fpga platform for convolutional neural network
Qiu · 2016
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Xnor-net: Imagenet classification using binary convolutional neural networks
M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi · 2016
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Learning efficient convolutional networks through network slimming
Z. Liu, J. Li, Z. Shen, G. Huang, S. Yan, and C. Zhang · 2017
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Thinet: A filter level pruning method for deep neural network compression
J.-H. Luo, J. Wu, and W. Lin · 2017
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An automatic rtl compiler for high-throughput fpga implementation of diverse deep convolutional neural networks
Y. Ma, Y. Cao, S. Vrudhula, and J. S. Seo · 2017
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Optimizing loop operation and dataflow in fpga acceleration of deep convolutional neural networks
Y. Ma, Y. Cao, S. Vrudhula, and J.-s. Seo · 2017
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End-to-end scalable fpga accelerator for deep residual networks
Y. Ma, M. Kim, Y. Cao, S. Vrudhula, J. S. Seo, Y. Ma, M. Kim, Y. Cao, S. Vrudhula, and J. S. Seo · 2017
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Exploring the regularity of sparse structure in convolutional neural networks
H. Mao, S. Han, J. Pool, W. Li, X. Liu, Y. Wang, and W. J. Dally · 2017
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Double MAC: doubling the performance of convolutional neural networks on modern fpgas
D. Nguyen, D. Kim, and J. Lee · 2017
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Can fpgas beat gpus in accelerating next-generation deep neural networks?
Nurvitadhi · 2017
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Scnn: An accelerator for compressed-sparse convolutional neural networks
A. Parashar, M. Rhu, A. Mukkara, A. Puglielli, R. Venkatesan, B. Khailany, J. Emer, S. W. Keckler, and W. J. Dally · 2017
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14.4 a scalable speech recognizer with deep-neural-network acoustic models and voice-activated power gating
M. Price, J. Glass, and A. P. Chandrakasan · 2017
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Sc-dcnn: Highly-scalable deep convolutional neural network using stochastic computing
A. Ren, Z. Li, C. Ding, Q. Qiu, Y. Wang, J. Li, X. Qian, and B. Yuan · 2017
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Escher: A cnn accelerator with flexible buffering to minimize off-chip transfer
Y. Shen, M. Ferdman, and P. Milder · 2017
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A new stochastic computing multiplier with application to deep convolutional neural networks
H. Sim and J. Lee · 2017
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How to train a compact binary neural network with high accuracy?
W. Tang, G. Hua, and L. Wang · 2017
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Hardware-software codesign of accurate, multiplier-free deep neural networks
H. Tann, S. Hashemi, I. Bahar, and S. Reda · 2017
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Finn: A framework for fast, scalable binarized neural network inference
Umuroglu · 2017
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Scaledeep: A scalable compute architecture for learning and evaluating deep networks
S. Venkataramani, A. Ranjan, S. Banerjee, D. Das, S. Avancha, A. Jagannathan, A. Durg, D. Nagaraj, B. Kaul, P. Dubey, and A. Raghunathan · 2017
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Fixed-point factorized networks
P. Wang and J. Cheng · 2017
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Automated systolic array architecture synthesis for high throughput cnn inference on fpgas
Wei · 2017
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Exploring heterogeneous algorithms for accelerating deep convolutional neural networks on fpgas
Xiao · 2017
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Aggregated residual transformations for deep neural networks
S. Xie, R. Girshick, P. Dollar, Z. Tu, and K. He · 2017
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Time: A training-in-memory architecture for memristor-based deep neural networks
H. Yang · 2017
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Shufflenet: An extremely efficient convolutional neural network for mobile devices
X. Zhang, X. Zhou, M. Lin, and J. Sun · 2017
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Accelerating binarized convolutional neural networks with software-programmable fpgas
Zhao · 2017
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Incremental network quantization: Towards lossless cnns with low-precision weights
A. Zhou, A. Yao, Y. Guo, L. Xu, and Y. Chen · 2017
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From hashing to cnns: Training binary weight networks via hashing
Q. Hu, P. Wang, and J. Cheng · 2018
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Block convolution: Towards memory-efficeint inference of large-scale cnns on fpga
G. Li, F. Li, T. Zhao, and J. Cheng · 2018
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Deepsearch: A fast image search framework for mobile devices
P. Wang, Q. Hu, Z. Fang, C. Zhao, and J. Cheng · 2018
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