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  Browse All Reviews > Computer Systems Organization (C) > Processor Architectures (C.1) > Other Architecture Styles (C.1.3)  
 
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  1-10 of 111 Reviews about "Other Architecture Styles (C.1.3)": Date Reviewed
  Detection of crop pests and diseases based on deep convolutional neural network and improved algorithm
Wu J., Li B., Wu Z.  ICMLT 2019 (Proceedings of the 2019 4th International Conference on Machine Learning Technologies, Nanchang, China, Jun 21-23, 2019) 20-27, 2019.  Type: Proceedings

Especially in large monoculture-based agricultural settings, an outbreak of pests or diseases can have a major impact on yield or quality of a crop. Advances in image processing based on convolutional neural network (CNN) architecture ...

Sep 29 2020
  Introduction to deep learning
Charniak E., The MIT Press, Cambridge, MA, 2019. 192 pp.  Type: Book (978-0-262039-51-2)

Deep learning has taken many application domains by storm, specifically those where artificial intelligence (AI) techniques have been struggling without too much success for decades. One of those domains is natural language processing ...

Mar 5 2020
  Grokking deep learning
Trask A., Manning Publications Co., Shelter Island, NY, 2019. 336 pp.  Type: Book (978-1-617293-70-2)

Deep learning is a hot topic in artificial intelligence (AI). It is exciting to see a book that can help readers understand the ideas of deep learning without advanced knowledge of mathematics....

Feb 14 2020
  Deep neural networks classification over encrypted data
Hesamifard E., Takabi H., Ghasemi M.  CODASPY 2019 (Proceedings of the Ninth ACM Conference on Data and Application Security and Privacy, Richardson, TX, Mar 25-27, 2019) 97-108, 2019.  Type: Proceedings

When we speak about a convolutional neural network (CNN) as a more complex deep learning algorithm, there are privacy-preserving issues that could be addressed in any study on the topic. In deep learning, CNNs are used to analyze compl...

Feb 13 2020
  Neural network classifiers using a hardware-based approximate activation function with a hybrid stochastic multiplier
Li B., Qin Y., Yuan B., Lilja D. ACM Journal on Emerging Technologies in Computing Systems 15(1): 1-21, 2019.  Type: Article

Li et al. present a novel approach for optimizing neural network implementations, that is, “a new architecture of stochastic neural networks” with a hidden approximate activation function and a hybrid stochastic mul...

May 1 2019
  A novel multilayer AAA model for integrated applications
Rezakhani A., Shirazi H., Modiri N. Neural Computing and Applications 29(10): 887-901, 2018.  Type: Article

Unidimensional static security policies cannot cater to the needs of a growing enterprise anymore. Local regulations, business processes, operational levels, and threat modeling are the key anchors around which successful organizations...

Mar 22 2019
  Reducing the performance gap between soft scalar CPUs and custom hardware with TILT
Tili I., Ovtcharov K., Steffan J. ACM Transactions on Reconfigurable Technology and Systems 10(3): 1-23, 2017.  Type: Article

This paper is an extension of a seminal presentation of thread- and instruction-level parallel template architecture (TILT). TILT is a software-programmable custom computing engine that utilizes both thread- and instruction-level paral...

Mar 9 2018
  Fast rendezvous on a cycle by agents with different speeds
Feinerman O., Korman A., Kutten S., Rodeh Y. Theoretical Computer Science 688 77-85, 2017.  Type: Article

Feinerman et al. provide illustrations concerned with the usefulness of different processing speeds and asynchrony of tasks of the operating agents in distributed computing systems. To investigate the rendezvous (meeting point) problem...

Oct 16 2017
  Cost-effective service provisioning for hybrid cloud applications
Liu F., Luo B., Niu Y. Mobile Networks and Applications 22(2): 153-160, 2017.  Type: Article

Making automatic, optimal workload placements in a multi-cloud, hybrid-cloud information technology (IT) infrastructure is an important problem that concerns DevOps. Here is one specific solution to the use case of social networking se...

Jun 12 2017
  Mobile demand profiling for cellular cognitive networking
Furno A., Naboulsi D., Stanica R., Fiore M. IEEE Transactions on Mobile Computing 16(3): 772-786, 2017.  Type: Article

Furno et al. describe a framework for automated demand profiling in mobile networks. Since participants in mobile communications can move around, both spatial as well as temporal characteristics of network traffic must be considered. I...

May 16 2017
 
 
 
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