Fetching the paper…
Reading the bibliography…
Convolutional neural networks (CNNs) have revolutionized the world of computer vision over the last few years, pushing image classification beyond human accuracy.
S. Gould, R. Fulton, and D. Koller, “Decomposing a scene into geometric and semantically consistent regions,” in ICCV , 2009
2009
Earlier work this paper cites.
C. Farabet, B. Martini, B. Corda, P. Akselrod, E. Culurciello, and Y. LeCun, “Neuflow: A runtime reconfigurable dataflow processor for vision,” in CVPR 2011 WORKSHOPS , June 2011, pp. 109–116
2011
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems , 2012, pp. 1097–1105
2012
Earlier work this paper cites.
P. H. Pham, D. Jelaca, C. Farabet, B. Martini, Y. LeCun, and E. Culurciello, “Neuflow: Dataflow vision processing system-on-a-chip,” in 2012 IEEE 55th International Midwest Symposium on Circuits and Systems (MWSCAS) , Aug 2012, pp. 1044–1047
2012
Earlier work this paper cites.
A. Coates et al. , “Deep learning with cots hpc systems,” in Proceedings of the 30th International Conference on Machine Learning (ICML-13) , vol. 28, no. 3. JMLR Workshop and Conference Proceedings, May 2013, pp. 1337–1345
2013
Earlier work this paper cites.
C. Farabet, C. Couprie, L. Najman, and Y. LeCun, “Learning hierarchical features for scene labeling,” IEEE transactions on pattern analysis and machine intelligence , vol. 35, no. 8, pp. 1915–1929, 2013
2013
Earlier work this paper cites.
W. Qadeer, R. Hameed, O. Shacham, P. Venkatesan, C. Kozyrakis, and M. A. Horowitz, “Convolution Engine : Balancing Efficiency & Flexibility in Specialized Computing,” in ISCA , 2013, pp. 24–35
2013
Earlier work this paper cites.
L. Wan et al. , “Regularization of neural networks using dropconnect,” in Proceedings of the 30th International Conference on Machine Learning (ICML-13) , vol. 28, no. 3. JMLR Workshop and Conference Proceedings, May 2013, pp. 1058–1066
2013
Earlier work this paper cites.
Y. Taigman, M. Yang, M. Ranzato, and L. Wolf, “Deepface: Closing the gap to human-level performance in face verification,” in Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on. IEEE , June 2014, pp. 1701–1708
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
J. Weston, S. Chopra, and A. Bordes, “Memory Networks,” ArXiv:1410.3916 , Oct. 2014
2014
Earlier work this paper cites.
B. Graham, “Fractional Max-Pooling,” ArXiv:1412.607 , Dec. 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
Movidius, “Ins-03510-c1 datasheet,” 2014, datasheet of Myriad 2 Vision Processor. [Online]. Available: http://uploads.movidius.com/1441734401-Myriad-2-product-brief.pdf
2014
Earlier work this paper cites.
V. Gokhale, J. Jin, A. Dundar, B. Martini, and E. Culurciello, “A 240 G-ops/s Mobile Coprocessor for Deep Neural Networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops , 2014, pp. 682–687
2014
Earlier work this paper cites.
T. Chen, Z. Du, N. Sun, J. Wang, C. Wu, Y. Chen, and O. Temam, “Diannao: A small-footprint high-throughput accelerator for ubiquitous machine-learning,” SIGARCH Comput. Archit. News , vol. 42, no. 1, pp. 269–284, Feb. 2014
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
V. Mnih et al. , “Human-level control through deep reinforcement learning,” Nature , vol. 518, no. 7540, pp. 529–533, Feb 2015, letter
2015
Cited alongside, same era.
L. Cavigelli, M. Magno, and L. Benini, “Accelerating real-time embedded scene labeling with convolutional networks,” in Proceedings of the 52nd Annual Design Automation Conference , ser. DAC ’15. New York, NY, USA: ACM, 2015, pp. 108:1–108:6
2015
Cited alongside, same era.
K. Ovtcharov, O. Ruwase, J.-Y. Kim, J. Fowers, K. Strauss, and E. S. Chung, “Accelerating deep convolutional neural networks using specialized hardware,” Microsoft Research, Tech. Rep., February 2015
2015
Cited alongside, same era.
F. Conti and L. Benini, “A Ultra-Low-Energy Convolution Engine for Fast Brain-Inspired Vision in Multicore Clusters,” in Proceedings of the 2015 Design, Automation & Test in Europe Conference & Exhibition , 2015
2015
Cited alongside, same era.
2016
Closest in time.
2016
Closest in time.
2016
Closest in time.
A. Teman, D. Rossi, P. Meinerzhagen, L. Benini, and A. Burg, “Power, area, and performance optimization of standard cell memory arrays through controlled placement,” ACM Transactions on Design Automation of Electronic Systems (TODAES) , vol. 21, no. 4, p. 59, 2016
2016
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Z. Du, R. Fasthuber, T. Chen, P. Ienne, L. Li, X. Feng, Y. Chen, and O. Temam, “ShiDianNao: Shifting Vision Processing Closer to the Sensor,” in ACM SIGARCH Computer Architecture News , 2015
2015
Cited alongside, same era.
2015
Cited alongside, same era.
M. Courbariaux, Y. Bengio, and J.-P. David, “BinaryConnect: Training Deep Neural Networks with binary weights during propagations,” in Advances in Neural Information Processing Systems , 2015, pp. 3105–3113
2015
Cited alongside, same era.
Z. Lin, M. Courbariaux, R. Memisevic, and Y. Bengio, “Neural Networks with Few Multiplications,” ICLR , 2015
2015
Cited alongside, same era.
S. Park, K. Bong, D. Shin, J. Lee, S. Choi, and H. J. Yoo, “A 1.93tops/w scalable deep learning/inference processor with tetra-parallel mimd architecture for big-data applications,” in 2015 IEEE International Solid-State Circuits Conference-(ISSCC) Digest of Technical Papers , Feb 2015, pp. 1–3
2015
Cited alongside, same era.
2015
Cited alongside, same era.
2015
Cited alongside, same era.
2015
Cited alongside, same era.
S. Park, S. Choi, J. Lee et al. , “A 126.1mw real-time natural ui/ux processor with embedded deep-learning core for low-power smart glasses,” in 2016 IEEE International Solid-State Circuits Conference (ISSCC) , Jan 2016, pp. 254–255
2016
Closest in time.
2016
Closest in time.
2016
Closest in time.
2016
Closest in time.
S. Chintala, “convnet-benchmarks,” 2016. [Online]. Available: https://github.com/soumith/convnet-benchmarks
2016
Closest in time.
N. Jouppi. (2016) Google supercharges machine learning tasks with tpu custom chip. [Online]. Available: https://cloudplatform.googleblog.com/2016/05/Google-supercharges-machine-learning-tasks-with-custom-chip.html
2016
Closest in time.
S. Jaehyeong et al. , “A 1.42tops/w deep convolutional neural network recognition processor for intelligent ioe systems,” in 2016 IEEE International Solid-State Circuits Conference (ISSCC) , April 2016
2016
Closest in time.
Y. H. Chen, T. Krishna, J. Emer, and V. Sze, “Eyeriss: An energy-efficient reconfigurable accelerator for deep convolutional neural networks,” in 2016 IEEE International Solid-State Circuits Conference (ISSCC) , Jan 2016, pp. 262–263
2016
Closest in time.
B. Reagen, P. Whatmough, R. Adolf, S. Rama, H. Lee, S. K. Lee, J. Hernández-Lobato, G.-Y. Wei, and D. Brooks, “Minerva: Enabling low-power, highly-accurate deep neural network accelerators,” in Proceedings of the 43rd International Symposium on Computer Architecture, ISCA , 2016
2016
Closest in time.
J. Albericio, P. Judd, T. Hetherington, T. Aamodt, N. E. Jerger, and A. Moshovos, “Cnvlutin: Ineffectual-neuron-free deep neural network computing,” in 2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA) , June 2016, pp. 1–13
2016
Closest in time.
2016
Closest in time.
R. LiKamWa, Y. Hou, J. Gao, M. Polansky, and L. Zhong, “Redeye: Analog convnet image sensor architecture for continuous mobile vision,” in Proceedings of ISCA , vol. 43, 2016
2016
Closest in time.
A. Shafiee, A. Nag, N. Muralimanohar, R. Balasubramonian, J. P. Strachan, M. Hu, R. S. Williams, and V. Srikumar, “Isaac: A convolutional neural network accelerator with in-situ analog arithmetic in crossbars,” in Proc. ISCA , 2016
2016
Closest in time.
A. Pullini, F. Conti, D. Rossi, I. Loi, M. Gautschi, and L. Benini, “A heterogeneous multi-core system-on-chip for energy efficient brain inspired vision,” in 2016 IEEE International Symposium on Circuits and Systems (ISCAS) . IEEE, 2016, pp. 2910–2910
2016
Closest in time.