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Recently ConvNets or convolutional neural networks (CNN) have come up as state-of-the-art classification and detection algorithms, achieving near-human performance in visual detection.
Handwritten digit recognition with a back-propagation network
B. B. Le Cun, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel · 1990
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The effects of quantization on multilayer neural networks
G. Dundar and K. Rose · 1994
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The mnist database of handwritten digits, 1998
Y. LeCun and C. Cortes · 1998
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The effects of quantization on multi-layer feedforward neural networks
M. Jiang and G. Gielen · 2003
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ImageNet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Learning multiple layers of features from tiny images
A. Krizhevsky · 2009
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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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Trading accuracy for power with an underdesigned multiplier architecture
P. Kulkarni, P. Gupta, and M. Ercegovac · 2011
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A flexible low power dsp with a programmable truncated multiplier
M. de la Guia Solaz, W. Han, and R. Conway · 2012
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ImageNet Classification with Deep Convolutional Neural Networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Cited alongside, same era.
Salsa: systematic logic synthesis of approximate circuits
S. Venkataramani, A. Sabne, V. Kozhikkottu, K. Roy, and A. Raghunathan · 2012
Cited alongside, same era.
Memory-centric accelerator design for convolutional neural networks
M. Peemen, A. A. Setio, B. Mesman, and H. Corporaal · 2013
Cited alongside, same era.
Quality programmable vector processors for approximate computing
S. Venkataramani, V. K. Chippa, S. T. Chakradhar, K. Roy, and A. Raghunathan · 2013
Caffe: Convolutional Architecture for Fast Feature Embedding
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Going Deeper with Convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2014
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Axnn: energy-efficient neuromorphic systems using approximate computing
S. Venkataramani, A. Ranjan, K. Roy, and A. Raghunathan · 2014
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A ultra-low-energy convolution engine for fast brain-inspired vision in multicore clusters
F. Conti and L. Benini · 2015
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Cited alongside, same era.
Diannao: A small-footprint high-throughput accelerator for ubiquitous machine-learning
T. Chen, Z. Du, N. Sun, J. Wang, C. Wu, Y. Chen, and O. Temam · 2014
Cited alongside, same era.
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Dvas: Dynamic voltage accuracy scaling for increased energy-efficiency in approximate computing
B. Moons and M. Verhelst · 2015
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Deep Image: Scaling up Image Recognition
R. Wu, S. Yan, Y. Shan, Q. Dang, and G. Sun · 2015
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