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The recent advent of `Internet of Things' (IOT) has increased the demand for enabling AI-based edge computing.
Generalization by weight-elimination with application to forecasting
Weigend et al · 1991
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Learning multiple layers of features from tiny images
Krizhevsky and Hinton · 2009
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Imagenet: A large-scale hierarchical image database
Deng et al · 2009
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Gpus and the future of parallel computing
Keckler et al · 2011
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Imagenet classification with deep convolutional neural networks
Krizhevsky et al · 2012
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Efficient estimation of word representations in vector space
Mikolov et al · 2013
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Going deeper with convolutions
Szegedy et al · 2015
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Fast r-cnn
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Human-level control through deep reinforcement learning
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Learning both weights and connections for efficient neural network
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Deep residual learning for image recognition
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Squeezenet: Alexnet-level accuracy with 50x fewer parameters and< 0.5 mb model size
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Compression-aware training of deep networks
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Soft weight-sharing for neural network compression
Ullrich et al · 2017
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Quantized neural networks: Training neural networks with low precision weights and activations
Hubara et al · 2017
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Ternary neural networks with fine-grained quantization
Mellempudi et al · 2017
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Hybrid binary networks: Optimizing for accuracy, efficiency and memory
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Prabhu et al · 2018
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