Nothing Special   »   [go: up one dir, main page]

Liao, 2001 - Google Patents

Neural networks in hardware: A survey

Liao, 2001

View PDF
Document ID
11961207110296154131
Author
Liao Y
Publication year
Publication venue
Department of Computer Science, University of California

External Links

Snippet

Over the past decade a large variety of hardware has been designed to exploit the inherent parallelism of the artificial neural network models. This paper presents an overview of neural network hardware. Neural network basics, hardware specification and performance …
Continue reading at citeseerx.ist.psu.edu (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/06Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/063Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
    • G06N3/0635Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means using analogue means
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/06Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/067Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using optical means
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/08Learning methods
    • G06N3/082Learning methods modifying the architecture, e.g. adding or deleting nodes or connections, pruning
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/04Architectures, e.g. interconnection topology
    • G06N3/0454Architectures, e.g. interconnection topology using a combination of multiple neural nets
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/04Architectures, e.g. interconnection topology
    • G06N3/0472Architectures, e.g. interconnection topology using probabilistic elements, e.g. p-rams, stochastic processors
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10Complex mathematical operations
    • G06F17/15Correlation function computation including computation of convolution operations
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06GANALOGUE COMPUTERS
    • G06G7/00Devices in which the computing operation is performed by varying electric or magnetic quantities
    • G06G7/12Arrangements for performing computing operations, e.g. operational amplifiers
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6267Classification techniques
    • G06K9/6268Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06JHYBRID COMPUTING ARRANGEMENTS
    • G06J1/00Hybrid computing arrangements
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F15/00Digital computers in general; Data processing equipment in general
    • G06F15/76Architectures of general purpose stored programme computers

Similar Documents

Publication Publication Date Title
Liao Neural networks in hardware: A survey
Misra et al. Artificial neural networks in hardware: A survey of two decades of progress
Bishop Neural networks and their applications
Battiti Accelerated backpropagation learning: Two optimization methods
Vanluchene et al. Neural networks in structural engineering
Graf et al. Analog electronic neural network circuits
Taylor The Promise of neural networks
Croall et al. Industrial applications of neural networks: project ANNIE handbook
Graupe Principles of artificial neural networks: basic designs to deep learning
Elmasry VLSI artificial neural networks engineering
Ponnapalli et al. A formal selection and pruning algorithm for feedforward artificial neural network optimization
RoyChowdhury et al. Dynamic tunneling technique for efficient training of multilayer perceptrons
Heemskerk Overview of neural hardware
Aybay et al. Classification of neural network hardware
Dogaru et al. A modified RBF neural network for efficient current-mode VLSI implementation
Svensson et al. Execution of neural network algorithms on an array of bit-serial processors
Miller et al. Office of Naval Research contributions to neural networks and signal processing in oceanic engineering
Kung et al. A unifying algorithm/architecture for artificial neural networks
Ortigosa et al. FPGA implementation of multi-layer perceptrons for speech recognition
Xue et al. Analyses of the hidden units of the multi-layer perceptron and its application in acoustic-to-articulatory mapping
Ramacher Guide lines to VLSI design of neural nets
Widrow et al. Thirty Years of Adaptive Neural Networks: Perceptron, Madaline, and Backpropagation
Singh et al. Hardware Implementation of Neural Network Using VHDL
Xue et al. A neural network weight pattern study with ECG pattern recognition
Amaldi et al. A review of combinatorial problems arising in feedforward neural network design