VLSI-SoC: Design and Engineering of Electronics Systems Based on New Computing Paradigms: 26th IFIP WG 10.5/IEEE International Conference on Very Large Scale Integration, VLSI-SoC 2018, Verona, Italy, October 8–10, 2018, Revised and Extended Selected Papers
暂无分享,去创建一个
Nicola Bombieri | Ricardo Reis | Graziano Pravadelli | Masahiro Fujita | Todd Austin | T. Austin | M. Fujita | R. Reis | N. Bombieri | G. Pravadelli
[1] Valentino Peluso,et al. Energy-Driven Precision Scaling for Fixed-Point ConvNets , 2018, 2018 IFIP/IEEE International Conference on Very Large Scale Integration (VLSI-SoC).
[2] Léon Bottou,et al. Large-Scale Machine Learning with Stochastic Gradient Descent , 2010, COMPSTAT.
[3] Donald Geman,et al. Coarse-to-Fine Face Detection , 2004, International Journal of Computer Vision.
[4] Alejandro D. Dominguez-Garcia,et al. Spoofing GPS Receiver Clock Offset of Phasor Measurement Units , 2013, IEEE Transactions on Power Systems.
[5] Massimo Alioto,et al. Energy-Quality Scalable Integrated Circuits and Systems: Continuing Energy Scaling in the Twilight of Moore’s Law , 2018, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.
[6] Valentino Peluso,et al. Scalable-Effort ConvNets for Multilevel Classification , 2018, 2018 IEEE/ACM International Conference on Computer-Aided Design (ICCAD).
[7] Michael J. Rycroft,et al. Understanding GPS. Principles and Applications , 1997 .
[8] A. El-Rabbany. Introduction to GPS: The Global Positioning System , 2002 .
[9] Andreas Moshovos,et al. Bit-Pragmatic Deep Neural Network Computing , 2016, 2017 50th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO).
[10] Slobodan Pajic,et al. Power System State Estimation and Contingency Constrained Optimal Power Flow - A Numerically Robust Implementation , 2007 .
[11] Avesta Sasan,et al. ICNN: An iterative implementation of convolutional neural networks to enable energy and computational complexity aware dynamic approximation , 2018, 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE).
[12] Muhammad Shafique,et al. Adaptive and Energy-Efficient Architectures for Machine Learning: Challenges, Opportunities, and Research Roadmap , 2017, 2017 IEEE Computer Society Annual Symposium on VLSI (ISVLSI).
[13] Jason Cong,et al. Scaling for edge inference of deep neural networks , 2018 .
[14] Srdjan Capkun,et al. On the requirements for successful GPS spoofing attacks , 2011, CCS '11.
[15] Marian Verhelst,et al. Energy-efficient ConvNets through approximate computing , 2016, 2016 IEEE Winter Conference on Applications of Computer Vision (WACV).
[16] Marian Verhelst,et al. 5 ENVISION : A 0 . 26-to-10 TOPS / W Subword-Parallel Dynamic-Voltage-Accuracy-Frequency-Scalable Convolutional Neural Network Processor in 28 nm FDSOI , 2017 .
[17] Michail Maniatakos,et al. Attacking the smart grid using public information , 2016, 2016 17th Latin-American Test Symposium (LATS).
[18] Vivienne Sze,et al. Designing Energy-Efficient Convolutional Neural Networks Using Energy-Aware Pruning , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[19] Vivienne Sze,et al. Efficient Processing of Deep Neural Networks: A Tutorial and Survey , 2017, Proceedings of the IEEE.
[20] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[21] Tara N. Sainath,et al. Convolutional, Long Short-Term Memory, fully connected Deep Neural Networks , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[22] Andrea Calimera,et al. Layer-Wise Compressive Training for Convolutional Neural Networks , 2018, Future Internet.
[23] Valentino Peluso,et al. Weak-MAC: Arithmetic Relaxation for Dynamic Energy-Accuracy Scaling in ConvNets , 2018, 2018 IEEE International Symposium on Circuits and Systems (ISCAS).
[24] Robinson Piramuthu,et al. HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).
[25] Marian Verhelst,et al. An Energy-Efficient Precision-Scalable ConvNet Processor in 40-nm CMOS , 2017, IEEE Journal of Solid-State Circuits.
[26] H. H. Happ,et al. Power System Control and Stability , 1979, IEEE Transactions on Systems, Man, and Cybernetics.
[27] Luca Benini,et al. GAP-8: A RISC-V SoC for AI at the Edge of the IoT , 2018, 2018 IEEE 29th International Conference on Application-specific Systems, Architectures and Processors (ASAP).
[28] Tara N. Sainath,et al. FUNDAMENTAL TECHNOLOGIES IN MODERN SPEECH RECOGNITION Digital Object Identifier 10.1109/MSP.2012.2205597 , 2012 .
[29] Kaushik Roy,et al. Conditional Deep Learning for energy-efficient and enhanced pattern recognition , 2015, 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE).
[30] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[31] Ting Liu,et al. Recent advances in convolutional neural networks , 2015, Pattern Recognit..
[32] Bo Chen,et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.
[33] Minxuan Zhang,et al. A Dynamic Multi-precision Fixed-Point Data Quantization Strategy for Convolutional Neural Network , 2016, NCCET.
[34] Trevor Darrell,et al. Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.
[35] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[36] Eric Blossom,et al. GNU radio: tools for exploring the radio frequency spectrum , 2004 .
[37] Liangzhen Lai,et al. Enabling Deep Learning at the LoT Edge , 2018, 2018 IEEE/ACM International Conference on Computer-Aided Design (ICCAD).
[38] Michail Maniatakos,et al. Open Source Intelligence for Energy Sector Cyberattacks , 2019, Advanced Sciences and Technologies for Security Applications.
[39] David A. Patterson,et al. In-datacenter performance analysis of a tensor processing unit , 2017, 2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA).
[40] Yoshua Bengio,et al. BinaryConnect: Training Deep Neural Networks with binary weights during propagations , 2015, NIPS.
[41] Sachin S. Talathi,et al. Fixed Point Quantization of Deep Convolutional Networks , 2015, ICML.
[42] Trevor Mudge,et al. 1 A 2 . 9 TOPS / W Deep Convolutional Neural Network SoC in FD-SOI 28 nm for Intelligent Embedded Systems , 2017 .
[43] N. Amjady,et al. Transient Stability Prediction by a Hybrid Intelligent System , 2007, IEEE Transactions on Power Systems.
[44] Pete Warden,et al. Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition , 2018, ArXiv.
[45] Yoshua Bengio,et al. Training deep neural networks with low precision multiplications , 2014 .
[46] Carey E. Noll,et al. The crustal dynamics data information system: A resource to support scientific analysis using space geodesy , 2010 .
[47] Marian Verhelst,et al. A 0.3–2.6 TOPS/W precision-scalable processor for real-time large-scale ConvNets , 2016, 2016 IEEE Symposium on VLSI Circuits (VLSI-Circuits).
[48] Luca Benini,et al. YodaNN: An Architecture for Ultralow Power Binary-Weight CNN Acceleration , 2016, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.
[49] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[50] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .