Conferences related to Neural network hardware

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2023 Annual International Conference of the IEEE Engineering in Medicine & Biology Conference (EMBC)

The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted full papers will be peer reviewed. Accepted high quality papers will be presented in oral and poster sessions,will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE.


2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

FUZZ-IEEE 2021 will represent a unique meeting point for scientists and engineers, both from academia and industry, to interact and discuss the latest enhancements and innovations in the field. The topics of the conference will cover all the aspects of theory and applications of fuzzy sets, fuzzy logic and associated approaches (e.g. aggregation operators such as the Fuzzy Integral), as well as their hybridizations with other artificial and computational intelligence techniques.


2020 IEEE International Conference on Image Processing (ICIP)

The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.


IECON 2020 - 46th Annual Conference of the IEEE Industrial Electronics Society

IECON is focusing on industrial and manufacturing theory and applications of electronics, controls, communications, instrumentation and computational intelligence.


2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS)

The IEEE International Midwest Symposium on Circuits and Systems is the oldest IEEE sponsored or co-sponsored conference in the area of analog and digital circuits and systems. Traditional lecture and interactive lecture/poster sessions cover virtually every area of electronic circuits and systems in all fields of interest to IEEE.


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Periodicals related to Neural network hardware

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Aerospace and Electronic Systems Magazine, IEEE

The IEEE Aerospace and Electronic Systems Magazine publishes articles concerned with the various aspects of systems for space, air, ocean, or ground environments.


Biomedical Circuits and Systems, IEEE Transactions on

The Transactions on Biomedical Circuits and Systems addresses areas at the crossroads of Circuits and Systems and Life Sciences. The main emphasis is on microelectronic issues in a wide range of applications found in life sciences, physical sciences and engineering. The primary goal of the journal is to bridge the unique scientific and technical activities of the Circuits and Systems ...


Circuits and Systems II: Express Briefs, IEEE Transactions on

Part I will now contain regular papers focusing on all matters related to fundamental theory, applications, analog and digital signal processing. Part II will report on the latest significant results across all of these topic areas.


Computational Intelligence Magazine, IEEE

The IEEE Computational Intelligence Magazine (CIM) publishes peer-reviewed articles that present emerging novel discoveries, important insights, or tutorial surveys in all areas of computational intelligence design and applications.


Computer Architecture Letters

Rigorously peer-reviewed forum for publishing early, high-impact results in the areas of uni- and multiprocessors computer systems, computer architecture workload characterization, performance evaluation and simulation techniques, and power-aware computing


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Most published Xplore authors for Neural network hardware

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Xplore Articles related to Neural network hardware

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IEE Colloquium on 'Hardware Implementation of Neural Networks and Fuzzy Logic' (Digest No.1994/061)

IEE Colloquium on Hardware Implementation of Neural Networks and Fuzzy Logic, 1994

None


Neural network hardware

[Proceedings 1992] IJCNN International Joint Conference on Neural Networks, 1992

Summary form only given, as follows. The author describes recent advances in the hardware implementation of neural networks, and gives his expectations for the future in this aspect of neural network technology.<<ETX>>


Neural Networks: Hardware Silicon For "Wetware" Algorithms

1991 Symposium on VLSI Technology, 1991

None


Learning Logic Functions Explicitly by Back-Propagation in NOR-Nets

IEEE International Workshop on Emerging Technologies and Factory Automation,, 1992

None


Reverse modeling of microwave circuits with bidirectional neural network models

IEEE Transactions on Microwave Theory and Techniques, 1998

Neural networks have been been developed into an alternative modeling approach for the microwave circuit-design process. In this paper, we describe a neural network-based microwave circuit-design approach that implements the solution- searching optimization routine by a modified neural network learning process. Both the development of a microwave circuit model and the searching of a design solution can thus take advantage ...


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Educational Resources on Neural network hardware

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IEEE.tv Videos

Improved Deep Neural Network Hardware Accelerators Based on Non-Volatile-Memory: the Local Gains Technique: IEEE Rebooting Computing 2017
Conversion of Artificial Recurrent Neural Networks to Spiking Neural Networks for Low-power Neuromorphic Hardware - Emre Neftci: 2016 International Conference on Rebooting Computing
Spiking Network Algorithms for Scientific Computing - William Severa: 2016 International Conference on Rebooting Computing
Co-Design of Algorithms & Hardware for DNNs - Vivienne Sze - LPIRC 2019
Spike Timing, Rhythms, and the Effective Use of Neural Hardware
Neural Processor Design Enabled by Memristor Technology - Hai Li: 2016 International Conference on Rebooting Computing
Lizhong Zheng's Globecom 2019 Keynote
Emergent Neural Network in reinforcement learning
Designing Reconfigurable Large-Scale Deep Learning Systems Using Stochastic Computing - Ao Ren: 2016 International Conference on Rebooting Computing
Overcoming the Static Learning Bottleneck - the Need for Adaptive Neural Learning - Craig Vineyard: 2016 International Conference on Rebooting Computing
High Throughput Neural Network based Embedded Streaming Multicore Processors - Tarek Taha: 2016 International Conference on Rebooting Computing
Network Analysis: RF Boot Camp
Behind Artificial Neural Networks
Computing with Dynamical Systems - Fred Rothganger: 2016 International Conference on Rebooting Computing
RNSnet: In-Memory Neural Network Acceleration Using Residue Number System - Sahand Salamat - ICRC 2018
Memory Centric Artificial Intelligence - Damien Querlioz at INC 2019
Learning to Dribble on a Real Robot by Success and Failure
A Comparator Design Targeted Towards Neural Net - David Mountain - ICRC San Mateo, 2019
Learning with Memristive Neural Networks: Neuromorphic Computing - Joshua Yang at INC 2019
Deep Learning Cookbook - Sergey Serebryakov - ICRC San Mateo, 2019

IEEE-USA E-Books

  • IEE Colloquium on 'Hardware Implementation of Neural Networks and Fuzzy Logic' (Digest No.1994/061)

    None

  • Neural network hardware

    Summary form only given, as follows. The author describes recent advances in the hardware implementation of neural networks, and gives his expectations for the future in this aspect of neural network technology.<<ETX>>

  • Neural Networks: Hardware Silicon For "Wetware" Algorithms

    None

  • Learning Logic Functions Explicitly by Back-Propagation in NOR-Nets

    None

  • Reverse modeling of microwave circuits with bidirectional neural network models

    Neural networks have been been developed into an alternative modeling approach for the microwave circuit-design process. In this paper, we describe a neural network-based microwave circuit-design approach that implements the solution- searching optimization routine by a modified neural network learning process. Both the development of a microwave circuit model and the searching of a design solution can thus take advantage of a hardware neural network processor, which is significantly faster than a software simulation. In addition, a systematic simulation-based approach to convert conventional circuit models into neural network models for this design process is described. The development of a heterojunction bipolar transistor (HBT) amplifier model and its applications are demonstrated.

  • Optoelectronic neural networks: mapping multilayer architectures on to an optoelectronic demonstrator

    In this paper we outline some of the changes needed to implement multilayer feed-forward neural networks using the demonstrator hardware which was based on around an array of vertical cavity surface emitting lasers. Network simulations show that the neural network demonstrator hardware can be used to implement two different classes of feed-forward network, the multilayer perceptron (MLP) and radial basis function (RBF) networks. In both cases, the actual training of the networks is performed offline using hardware simulations and the weighted interconnections between neurons are fixed before application to the optoelectronic hardware.

  • Reduction of necessary precision for the learning of pattern recognition

    The authors propose a novel learning algorithm with weighted error function (WEF). They have reduced the necessary precision for the learning of multi- font alpha-numeric recognition to 10-bit fixed point precision using the WEF. The WEF raises the recognition accuracy by more than 25% when the precision of all operations (including multiplication and addition) and the precision of all data (including weights and backpropagation signals) are limited to 10-bit fixed point. This improves the feasibility of analog implementation and lessens the data width of digital implementation. The performance of the WEF is high even with a small number of hidden neurons. This enables the reduction of weight memory. Furthermore, the WEF accelerates the learning and thus refines the adaptability of backpropagation.<<ETX>>

  • MATLAB-neural networks toolbox hardware post-processor

    The development of intelligent systems has created a need for reliable development techniques and algorithms for artificial neural networks (ANN) and fuzzy control techniques. The Neural Network Toolbox (now at v2.00) provides the first steps toward development of solutions that implement this technology. The output from the software is presented in easily understood graphical and numerical formats and the interactive MATLAB environment enables more creative thinking to be applied to problems. The developer is however faced with the problem of transferring work from MATLAB onto suitable hardware where further research and practical implementation may be conducted. This work seeks to use techniques which other aspects of engineering have used successfully to transfer information onto hardware for practical use. Current work at Lancaster involved with development of ANN architectures and control solutions also involves the use of hardware from Neural technologies, in particular the nt6000 system. Network design using standard back propagation designs is also carried out using MATLAB and the NN toolbox. The work discussed in this presentation shows how techniques were developed to allow the direct transfer of code from MATLAB to this hardware.

  • Unfully interconnected neural networks as associative memory

    Unfully interconnected neural networks (UINNs) are proposed as associative memory. The basic idea is to form compact internal representations of patterns in order to increase the storage efficiency of the interconnections. Several effective methods for designing UINNs as associative memory, including monolayered and multilayered neural networks, are presented. A maximum- interconnection-preserving method which forms a rectangular grid structure of local interconnections is proposed. Dynamical modeling almost doubles the average storage per interconnection weight of the neural network compared with the Hopfield model. Multilayered neural networks are of relatively high storage capacity.<<ETX>>

  • Implementing backpropagation with analog hardware

    The main problem when implementing backpropagation with analog hardware is the offset present in multipliers in the backward path of multilayer neural networks. Here this problem is further investigated and a solution is presented at the cost of the speed of the backpropagation algorithm.<<ETX>>



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