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Neural Design Network: Graphic Layout Generation with Constraints [article]

Hsin-Ying Lee, Lu Jiang, Irfan Essa, Phuong B Le, Haifeng Gong, Ming-Hsuan Yang, Weilong Yang
<span title="2020-07-16">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We propose a method for design layout generation that can satisfy user-specified constraints. The proposed neural design network (NDN) consists of three modules.  ...  Graphic design is essential for visual communication with layouts being fundamental to composing attractive designs.  ...  In this work, we introduce neural design network (NDN), a new approach of synthesizing a graphic design layout given a set of components with user-specified attributes and constraints.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1912.09421v2">arXiv:1912.09421v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/t77s4gw43rfadces7hkkms7pcu">fatcat:t77s4gw43rfadces7hkkms7pcu</a> </span>
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Neural network design of a banyan network controller

T.X. Brown, K.-H. Liu
<span title="">1990</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/onirm7ye2bfobnpwuwaopap5yu" style="color: black;">IEEE Journal on Selected Areas in Communications</a> </i> &nbsp;
All my experiences at Caltech are intimately intertwined with her. iv Abstract A neural network is a highly interconnected set of simple processors.  ...  Together these properties imply that we can build efficient massively parallel machines using neural networks.  ...  The fact that the constraint sets for the Banyan network controller are blocks can greatly simplify the layout.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/49.62821">doi:10.1109/49.62821</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nszkk6o2sje7rparwgi4oqmj3y">fatcat:nszkk6o2sje7rparwgi4oqmj3y</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20070824062222/http://ece-www.colorado.edu/~timxb/timxb/pubs/Thesis.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/b4/88/b4889f09c4f97110e85ff339ca2324786bd0253b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/49.62821"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Deep neural networks for the evaluation and design of photonic devices [article]

Jiaqi Jiang, Mingkun Chen, Jonathan A. Fan
<span title="2020-06-30">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We will also examine how deep generative networks can learn geometric features in device distributions and even be configured to serve as robust global optimizers.  ...  The data sciences revolution is poised to transform the way photonic systems are simulated and designed.  ...  Inverse design with discriminative networks. a| Backpropagation can be used with a trained discriminative neural network to perform gradient-based inverse design.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2007.00084v1">arXiv:2007.00084v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/s76z7d6ghfdd7c3pe6f5guhksa">fatcat:s76z7d6ghfdd7c3pe6f5guhksa</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200710050004/https://arxiv.org/pdf/2007.00084v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2007.00084v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Integrated optimal topology design and shape optimization using neural networks

A.R. Yildiz, N. Öztürk, N. Kaya, F. Öztürk
<span title="">2003</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/23zzy464pvfarebieeuobs627a" style="color: black;">Structural And Multidisciplinary Optimization</a> </i> &nbsp;
A neural network-based image-processing technique is presented to match the arbitrarily shaped holes inside the structure with predefined features.  ...  In this paper, neural network-and featurebased approaches are introduced to overcome current shortcomings in the automated integration of topology design and shape optimization.  ...  In general, parameters for a neural network architecture are determined by trial and error.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s00158-003-0300-0">doi:10.1007/s00158-003-0300-0</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wpyhtsz435gwpnleclghu5xpm4">fatcat:wpyhtsz435gwpnleclghu5xpm4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20110725032512/http://www20.uludag.edu.tr:80/%7Ealiriza/topologyoptimization.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/d3/8b/d38bcf1542520d8429033687a70004d06df9c94e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s00158-003-0300-0"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Modern Art Interactive Design Relying on Neural Network Intelligent Algorithms

Yanan Guo, Qiangyi Li
<span title="2022-03-09">2022</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4xiibn6qsbh3pj5zdvqplxb37m" style="color: black;">Advances in Multimedia</a> </i> &nbsp;
exchange place to integrate the artificial intelligence technology after the exchange, and interacts with the network artificial intelligence technology and the multimedia multielement exchange art in  ...  It has been proved through practice that artificial intelligence technology embodies the advantages of design tools and the improvement of efficiency in modern art exchange; it allows the current diversity  ...  By means of the integrated design style, a page layout that humans love can be created according to the corresponding visual space configuration, color, design principles, and cognitive methods of the  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2022/6589417">doi:10.1155/2022/6589417</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fu24mrrcnfas7iind5vhgs7wxq">fatcat:fu24mrrcnfas7iind5vhgs7wxq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220310110659/https://downloads.hindawi.com/journals/am/2022/6589417.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/1c/45/1c450d4cc5fef197ee47ead28ab29c651dcfb03d.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2022/6589417"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> hindawi.com </button> </a>

Data-Driven Dynamic Neural Programming for Network Media Nonlinear Visual Communication Design

Jing Yang, Xiaoman Li, Gengxin Sun
<span title="2022-02-21">2022</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wpareqynwbgqdfodcyhh36aqaq" style="color: black;">Mathematical Problems in Engineering</a> </i> &nbsp;
This paper mainly studies how to use data-driven nonlinear dynamic neural programming method to realize visual communication design in network media environment.  ...  of the system with the real output value of the object is found that they are very close, which shows that the dynamic neural programming method is more complex and nonlinear.  ...  At this time, the data becomes a kind of meaning representation, generating graphics with a conveying purpose.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2022/6283902">doi:10.1155/2022/6283902</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/klf3copp6zax3fy2nmauytbnrq">fatcat:klf3copp6zax3fy2nmauytbnrq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220424135216/https://downloads.hindawi.com/journals/mpe/2022/6283902.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/f7/50/f7505511d54770bb9b8ba6832ac5fde731b9e071.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2022/6283902"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> hindawi.com </button> </a>

Design of Task-Specific Optical Systems Using Broadband Diffractive Neural Networks [article]

Yi Luo, Deniz Mengu, Nezih T. Yardimci, Yair Rivenson, Muhammed Veli, Mona Jarrahi, Aydogan Ozcan
<span title="2019-09-14">2019</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/y5icnkvcrncrdfvhmtw3bdbma4" style="color: black;">Light: Science &amp; Applications</a> </i> &nbsp; <span class="release-stage" >pre-print</span>
Merging the native or engineered dispersion of various material systems with a deep learning-based design strategy, broadband diffractive neural networks help us engineer light-matter interaction in 3D  ...  We report a broadband diffractive optical neural network design that simultaneously processes a continuum of wavelengths generated by a temporally-incoherent broadband source to all-optically perform a  ...  We approach this general broadband inverse optical design problem from the perspective of diffractive optical neural network training and demonstrate its success with various optical tasks.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1038/s41377-019-0223-1">doi:10.1038/s41377-019-0223-1</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31814969">pmid:31814969</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6885516/">pmcid:PMC6885516</a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1909.06553v1">arXiv:1909.06553v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/byyehuomebbvbpclukhkstck7q">fatcat:byyehuomebbvbpclukhkstck7q</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200427231050/https://arxiv.org/pdf/1909.06553v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/12/8d/128d79aec914bee46a57bcac554d4ee0d3175c6a.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1038/s41377-019-0223-1"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1909.06553v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Towards Design Space Exploration and Optimization of Fast Algorithms for Convolutional Neural Networks (CNNs) on FPGAs [article]

Afzal Ahmad, Muhammad Adeel Pasha
<span title="2019-03-05">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Convolutional Neural Networks (CNNs) have gained widespread popularity in the field of computer vision and image processing.  ...  Furthermore, we obtain savings of up to 53.6% in logic resources compared with the state-of-the-art implementation.  ...  CONVOLUTIONAL NEURAL NETWORKS (CNNS) CNNs are a special class of deep neural networks used for object detection in images.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1903.01811v1">arXiv:1903.01811v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dkzq2xdqljbbzlcgzumomkcose">fatcat:dkzq2xdqljbbzlcgzumomkcose</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200823183549/https://arxiv.org/pdf/1903.01811v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/d5/a0/d5a0b37700b047aebd651ce107c6959f245aad32.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1903.01811v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

A Construction Kit for Efficient Low Power Neural Network Accelerator Designs [article]

Petar Jokic, Erfan Azarkhish, Andrea Bonetti, Marc Pons, Stephane Emery, Luca Benini
<span title="2021-06-24">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Implementing embedded neural network processing at the edge requires efficient hardware acceleration that couples high computational performance with low power consumption.  ...  Reported optimizations range from up to 10'000x memory savings to 33x energy reductions, providing chip designers an overview of design choices for implementing efficient low power neural network accelerators  ...  Neural network notations Fig. 5 illustrates a generic NN layer along with the notations of layer dimensions that we use throughout the paper.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2106.12810v1">arXiv:2106.12810v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gx7cspazc5fdfoi64t2zjth7am">fatcat:gx7cspazc5fdfoi64t2zjth7am</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210628201157/https://arxiv.org/ftp/arxiv/papers/2106/2106.12810.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/df/66/df6604103779a0d3cb4b98cfaf0bc0ce5cd7cade.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2106.12810v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Gas Turbine Engine Control Design Using Fuzzy Logic and Neural Networks

M. Bazazzadeh, H. Badihi, A. Shahriari
<span title="">2011</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/75xpl4z7arculfjaok5ombj2ju" style="color: black;">International Journal of Aerospace Engineering</a> </i> &nbsp;
These parameters provide a precious database, which train a neural network.  ...  At the next step, we propose a FLC by using the engine simulation model and the neural network results. The proposed control scheme is proved by computer simulation using the designed engine model.  ...  This is effective to design the best controller. The designed neural network simulated the engine performance backwardly.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2011/156796">doi:10.1155/2011/156796</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vttjnwdscffdtlyq73ryhg6lqq">fatcat:vttjnwdscffdtlyq73ryhg6lqq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180724120044/http://downloads.hindawi.com/journals/ijae/2011/156796.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ce/6a/ce6a57eaccd6d88890b1b0918af0ddae2b66e397.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2011/156796"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> hindawi.com </button> </a>

An Introduction to Recursive Neural Networks and Kernel Methods for Cheminformatics

Alessio Micheli, Alessandro Sperduti, Antonina Starita
<span title="2007-05-01">2007</span> <i title="Bentham Science Publishers Ltd."> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/xtiwjcbzsbd35jtqmz56mrpmb4" style="color: black;">Current pharmaceutical design</a> </i> &nbsp;
The aim of this paper is to introduce the reader to new developments in Neural Networks and Kernel Machines concerning the treatment of structured domains.  ...  Specifically, we discuss the research on these relatively new models to introduce a novel and more general approach to QSPR/QSAR analysis.  ...  Specifically, neural networks models can be characterized by the design of the neural units and the topology of the network of units, i.e. the layout of the architecture.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2174/138161207780765981">doi:10.2174/138161207780765981</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/17504168">pmid:17504168</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/melxr62d6fhl5p23ki6faztitm">fatcat:melxr62d6fhl5p23ki6faztitm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20080222001716/http://www.math.unipd.it/~sperduti/PAPERI/micheli-sperduti-starita2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/7d/11/7d119dc0690f9cd554b272fbb412a4e57cffb9d7.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2174/138161207780765981"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Optimal design of systems that evolve over time using neural networks

Michael K. Nolan, Richard T. Howard, Robert D. Richards
<span title="2007-04-27">2007</span> <i title="SPIE"> Sensors and Systems for Space Applications </i> &nbsp;
In this study, a neural network was able to recognize a pattern whereby flexible sensor networks evolved more successfully than less flexible networks.  ...  Optimal design solutions for the small network do not necessarily lead to optimal design solutions for the larger network.  ...  Modeling the network The network is first designed with 2 layers.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1117/12.719092">doi:10.1117/12.719092</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3qyse5tg2zgl7oodlmo2njuztm">fatcat:3qyse5tg2zgl7oodlmo2njuztm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20120124172343/http://strategic.mit.edu/docs/SM-20-Nolan-M-2005.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/fb/13/fb1392856a2a8132fc617f0df42f848408a0fe0f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1117/12.719092"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

DNNZip: Selective Layers Compression Technique in Deep Neural Network Accelerators

Habiba Lahdhiri, Maurizio Palesi, Salvatore Monteleone, Davide Patti, Giuseppe Ascia, Jordane Lorandel, Emmanuelle Bourdel, Vincenzo Catania
<span title="">2020</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/6nxn3oxzcveorfagbcbilem2ay" style="color: black;">2020 23rd Euromicro Conference on Digital System Design (DSD)</a> </i> &nbsp;
Seine Initiative for Excellence ("Investissements d'Avenir" ANR-16-IDEX-0008); (iii) the DIEEI at University of Catania within the research program "Piano per la Ricerca 2016/2018"; (iv) the HiPEAC5 Network  ...  The decompression unit has been synthesized at gate level with Synopsys Design Compiler and implemented with Synopsys IC Compiler.  ...  The links have been modelled with HSPICE and the parasitics extraction from layout has been made using Cadence Virtuoso.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/dsd51259.2020.00088">doi:10.1109/dsd51259.2020.00088</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/dsd/LahdhiriPMPALBC20.html">dblp:conf/dsd/LahdhiriPMPALBC20</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/u4ympnf365axvjdewf7xzcg2zq">fatcat:u4ympnf365axvjdewf7xzcg2zq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201108161757/https://hal.archives-ouvertes.fr/hal-02906973/document" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/11/0d/110da74e2d1f5e856dd29a2a7cc6686c9ac3ab1b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/dsd51259.2020.00088"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Neural Network-Based Thermal Simulation of Integrated Circuits on GPUs

Arvind Sridhar, Alessandro Vincenzi, Martino Ruggiero, David Atienza
<span title="">2012</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/rl7xk4fwazdrred2difr6v3lii" style="color: black;">IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems</a> </i> &nbsp;
This is achieved by parallelizing the computation-intensive task of transient temperature tracking using neural networks and exploiting the computational power of massively parallel graphics processing  ...  Speedups scale with the size of the 3-D multiprocessor ICs and our proposed method serves as a valuable design space exploration tool.  ...  In this context, neural networks (NNs) can represent an optimal parallelizable solution to this problem.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tcad.2011.2174236">doi:10.1109/tcad.2011.2174236</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/b5tvgrq6ingttigt7rfk6gkqxa">fatcat:b5tvgrq6ingttigt7rfk6gkqxa</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170809120118/https://infoscience.epfl.ch/record/169825/files/TCAD2012-06106739.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/24/61/24619dbcdcdd71ab4f9f43e0017290a82edbe324.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tcad.2011.2174236"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Low Bit-width Convolutional Neural Network on RRAM

Yi Cai, Tianqi Tang, Lixue Xia, Boxun Li, Yu Wang, Huazhong Yang
<span title="">2019</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/rl7xk4fwazdrred2difr6v3lii" style="color: black;">IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems</a> </i> &nbsp;
In addition, we propose a splitting and quantizing while training method to incorporate the actual hardware constraints with the training.  ...  The emerging resistive random-access memory (RRAM) has been widely applied in accelerating the computing of deep neural networks.  ...  RRAM-based Neural Network Accelerators In previous studies, designs were proposed to accelerate neural network computing based on RRAM, ranging from the circuit designs to the high-level architecture designs  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tcad.2019.2917852">doi:10.1109/tcad.2019.2917852</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wsidwhfgczbolbnlpgwhapdtue">fatcat:wsidwhfgczbolbnlpgwhapdtue</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200228004426/https://pdfs.semanticscholar.org/e268/1315d820cebaa6819cd955f4380da11e256e.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/e2/68/e2681315d820cebaa6819cd955f4380da11e256e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tcad.2019.2917852"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>
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