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CascadeCNN: Pushing the Performance Limits of Quantisation in Convolutional Neural Networks [article]

Alexandros Kouris, Stylianos I. Venieris, Christos-Savvas Bouganis
2018 arXiv   pre-print
A two-stage architecture tailored for any given CNN-FPGA pair is generated, consisting of a low- and high-precision unit in a cascade.  ...  This work presents CascadeCNN, an automated toolflow that pushes the quantisation limits of any given CNN model, aiming to perform high-throughput inference.  ...  ACKNOWLEDGMENT The support of the EPSRC Centre for Doctoral Training in High Performance Embedded and Distributed Systems (HiPEDS, Grant Reference EP/L016796/1) is gratefully acknowledged.  ... 
arXiv:1807.05053v1 fatcat:bn3sz2ewgjgfrgsmpzymnfmk5y

SPINN: Synergistic Progressive Inference of Neural Networks over Device and Cloud [article]

Stefanos Laskaridis, Stylianos I. Venieris, Mario Almeida, Ilias Leontiadis, Nicholas D. Lane
2020 arXiv   pre-print
Despite the soaring use of convolutional neural networks (CNNs) in mobile applications, uniformly sustaining high-performance inference on mobile has been elusive due to the excessive computational demands  ...  The proposed system introduces a novel scheduler that co-optimises the early-exit policy and the CNN splitting at run time, in order to adapt to dynamic conditions and meet user-defined service-level requirements  ...  Similarly, classifier cascades [21, 33, 38, 39, 71] require multiple models to obtain performance gains.  ... 
arXiv:2008.06402v1 fatcat:7vtsf2qxyvd6loo4as7hnzjaoe

Deep Neural Network Approximation for Custom Hardware: Where We've Been, Where We're Going [article]

Erwei Wang, James J. Davis, Ruizhe Zhao, Ho-Cheung Ng, Xinyu Niu, Wayne Luk, Peter Y. K. Cheung, George A. Constantinides
2019 arXiv   pre-print
Research has shown that custom hardware-based neural network accelerators can surpass their general-purpose processor equivalents in terms of both throughput and energy efficiency.  ...  This article represents the first survey providing detailed comparisons of custom hardware accelerators featuring approximation for both convolutional and recurrent neural networks, through which we hope  ...  For an AlexNet implementation classifying ImageNet, TWNs achieved a 46% top-one error rate: lower than all binarised neural networks reported thus far.  ... 
arXiv:1901.06955v3 fatcat:rkgo2oisdrgv3dtnbtlldlkpba

Artificial Intelligence based Sensor Data Analytics Framework for Remote Electricity Network Condition Monitoring [article]

Tharmakulasingam Sirojan
2021 arXiv   pre-print
Finally, a deep neural network-based energy disaggregation framework is developed to separate the load specific energy usage from an aggregated signal.  ...  The development of such reliable networks with better energy demand management will rely on having an integrated network-wide condition monitoring system.  ...  Such variations in the layers and its arrangement lead to different deep learning architectures such as fully connected deep neural networks (DNN), convolutional deep neural networks (CNN), recurrent neural  ... 
arXiv:2102.03356v1 fatcat:mb42q6i7craptoulvkt3cvy22e

Continuous Human Action Recognition for Human-Machine Interaction: A Review [article]

Harshala Gammulle, David Ahmedt-Aristizabal, Simon Denman, Lachlan Tychsen-Smith, Lars Petersson, Clinton Fookes
2022 arXiv   pre-print
With advances in data-driven machine learning research, a wide variety of prediction models have been proposed to capture spatio-temporal features for the analysis of video streams.  ...  We investigate the application of such models to real-world scenarios and discuss several limitations and key research directions towards improving interpretability, generalisation, optimisation and deployment  ...  They learn a series of cascade modules which progressively learn weak-to-strong frame level classifiers.  ... 
arXiv:2202.13096v1 fatcat:mczyeb5vyfgxdiubjhklwjrtlm

Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion

Reem Ibrahim Hasan, Suhaila Mohd Yusuf, Laith Alzubaidi
2020 Plants  
It is currently playing a vital role in the early detection and classification of plant diseases.  ...  and their effects on classifier accuracy.  ...  Conflicts of Interest: The authors declare that they have no conflicts of interest.  ... 
doi:10.3390/plants9101302 pmid:33019765 pmcid:PMC7599890 fatcat:upbgnxc4azhi3frpzdmaaasqsy

Stochastic computing in convolutional neural network implementation: a review

Yang Yang Lee, Zaini Abdul Halim
2020 PeerJ Computer Science  
However, presently, SC started to regain interest after the widespread of deep learning application, specifically the convolutional neural network (CNN) algorithm due to its practicality in hardware implementation  ...  An evolution of CNN, namely, binarised neural network, had also gained attention in the edge computing due to its compactness and computing efficiency.  ...  This research was funded by the School of Electrical and Electronic Engineering, Universiti Sains Malaysia (1001/PELECT/8014152).  ... 
doi:10.7717/peerj-cs.309 pmid:33816960 pmcid:PMC7924419 fatcat:ejgavuqryrferd5hitioj4acxu

AI and ML – Enablers for Beyond 5G Networks

Alexandros Kaloxylos, Anastasius Gavras, Daniel Camps Mur, Mir Ghoraishi, Halid Hrasnica
2020 Zenodo  
Feed-forward neural networks, deep neural networks, recurrent neural networks, and convolutional neural networks belong to this family.  ...  A family of neural networks is presented, which are generally speaking, non-linear statistical data modelling and decision making tools.  ...  Convolutional neural networks Generic Convolutional Neural Networks (CNNs) [164] are a specialized kind of deep learning structure that can infer local patterns in the feature space of a matrix input  ... 
doi:10.5281/zenodo.4299895 fatcat:ngzbopfm6bb43lnrmep6nz5icm

2022 Roadmap on Neuromorphic Computing and Engineering [article]

Dennis V. Christensen, Regina Dittmann, Bernabé Linares-Barranco, Abu Sebastian, Manuel Le Gallo, Andrea Redaelli, Stefan Slesazeck, Thomas Mikolajick, Sabina Spiga, Stephan Menzel, Ilia Valov, Gianluca Milano (+47 others)
2022 arXiv   pre-print
The aim of this Roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic  ...  The Roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges.  ...  concerns are increasingly pressuring our way of life, become an essential component of a sustainable society.  ... 
arXiv:2105.05956v3 fatcat:pqir5infojfpvdzdwgmwdhsdi4

Program

2021 2021 National Conference on Communications (NCC)  
Nonetheless, not every problem can and should be solved using deep neural networks (DNNs).  ...  A pseudorandom sequence family designed in a 1996 paper co-authored by him formed the short scrambling code of the 3G WCDMA cellular standard.  ...  The classification is performed using a neural network that is designed with only convolutional layers.  ... 
doi:10.1109/ncc52529.2021.9530194 fatcat:ahdw5ezvtrh4nb47l2qeos3dwq

Towards 6G-enabled Internet of Vehicles: Security and Privacy

D. P. Moya Osorio, I. Ahmad, J. D. Vega Sanchez, A. Gurtov, J. Scholliers, M. Kutila, P. Porambage
2022 IEEE Open Journal of the Communications Society  
The conceptualisation of the sixth generation of mobile wireless networks (6G) has already started with some potential disruptive technologies resonating as enablers for driving the emergence of a number  ...  In this paper, we provide a timely deliberation of the role that promissory 6G enabling technologies such as artificial intelligence, network softwarisation, network slicing, blockchain, edge computing  ...  Their proposed two-layer FL model is based on convolutional neural network that uses global and local context of vehicles and RSUs to perform heterogenous and hierarchical model selection and the aggregation  ... 
doi:10.1109/ojcoms.2022.3143098 fatcat:vfuwuncr3vaotly7yw7yx25xyu

A Review of Computer Vision Methods in Network Security [article]

Jiawei Zhao, Rahat Masood, Suranga Seneviratne
2020 arXiv   pre-print
On the other hand, recent years witnessed a phenomenal growth in computer vision mainly driven by the advances in the area of convolutional neural networks.  ...  At a glance, it is not trivial to see how computer vision methods are related to network security.  ...  The raw static byte code inputs to a convolutional neural network followed by a recurrent neural network.  ... 
arXiv:2005.03318v1 fatcat:pcng7535obec3l6fejkllbi3ii

Field Programmable Gate Array Applications—A Scientometric Review

Juan Ruiz-Rosero, Gustavo Ramirez-Gonzalez, Rahul Khanna
2019 Computation  
Field Programmable Gate Array (FPGA) is a general purpose programmable logic device that can be configured by a customer after manufacturing to perform from a simple logic gate operations to complex systems  ...  Also, we present an evolution and trend analysis of the related applications.  ...  A survey of FPGA-based accelerators for convolutional neural networks. Neural Comput. Appl. 2018. [CrossRef] 50. Blaiech, A.G.; Khalifa, K.B.; Valderrama, C.; Fernandes, M.A.; Bedoui, M.H.  ... 
doi:10.3390/computation7040063 fatcat:wxtatzsvvnfopghdfl25hcfc2a

2020 Index IEEE Transactions on Vehicular Technology Vol. 69

2020 IEEE Transactions on Vehicular Technology  
., TVT Dec. 2020 16168-16172 Hong, C., Shan, H., Song, M., Zhuang, W., Xiang, Z., Wu, Y., and Yu, X., A Joint Design of Platoon Communication and Control Based on LTE-V2V; 15893-15907 Hong, C.S., see  ...  Hou, C., see Tu, Y., TVT Sept. 2020 10085-10089 Hou, F., see Qian, B., TVT June 2020 6374-6387 Hou, L., see Yao, Y., TVT Oct. 2020 10750-10758 Hou, T., Liu, Y., Song, Z., Sun, X., and Chen, Y., Networks  ...  ., +, TVT Dec. 2020 15880- 15892 Development of Artificial Intelligence to Classify Quality of Transmission Shift Control Using Deep Convolutional Neural Networks.  ... 
doi:10.1109/tvt.2021.3055470 fatcat:536l4pgnufhixneoa3a3dibdma

Proceedings of the 2020 Connecting the Dots Workshop

David Lange
2020 Zenodo  
Complete set of all proceedings contributed and reviewed for the 2020 Connecting the Dots workshop.  ...  Acknowledgements The authors would like to extend a heartfelt thank you to Andreas Hoecker for his support.  ...  ACKNOWLEDGEMENTS ACKNOWLEDGEMENTS Part of this work was conducted at "iBanks", the AI GPU cluster at Caltech.  ... 
doi:10.5281/zenodo.4088760 fatcat:kulhvq3t5fglnimqawge7lnt4q
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