[go: up one dir, main page]

Chatterjee et al., 2017 - Google Patents

Towards optimal quantization of neural networks

Chatterjee et al., 2017

View PDF
Document ID
4187577758737284984
Author
Chatterjee A
Varshney L
Publication year
Publication venue
2017 IEEE International Symposium on Information Theory (ISIT)

External Links

Snippet

Due to the unprecedented success of deep neural networks in inference tasks like speech and image recognition, there has been increasing interest in using them in mobile and in- sensor applications. As most current deep neural networks are very large in size, a major …
Continue reading at www.c3sr.com (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/08—Learning methods
    • G06N3/082—Learning methods modifying the architecture, e.g. adding or deleting nodes or connections, pruning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/063—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
    • G06N3/0635—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means using analogue means
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/12—Computer systems based on biological models using genetic models
    • G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/04—Architectures, e.g. interconnection topology
    • G06N3/0454—Architectures, e.g. interconnection topology using a combination of multiple neural nets
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/04—Inference methods or devices
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computer systems based on specific mathematical models
    • G06N7/005—Probabilistic networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/02—Knowledge representation
    • G06N5/022—Knowledge engineering, knowledge acquisition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247—Extracting 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computer systems based on specific mathematical models
    • G06N7/02—Computer systems based on specific mathematical models using fuzzy logic
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6251—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on a criterion of topology preservation, e.g. multidimensional scaling, self-organising maps

Similar Documents

Publication Publication Date Title
EP3543917B1 (en) Dynamic adaptation of deep neural networks
Goel et al. A survey of methods for low-power deep learning and computer vision
Hubara et al. Binarized neural networks
Hubara et al. Quantized neural networks: Training neural networks with low precision weights and activations
Tjandra et al. Compressing recurrent neural network with tensor train
US20200134461A1 (en) Dynamic adaptation of deep neural networks
Rastegari et al. Xnor-net: Imagenet classification using binary convolutional neural networks
CN109445935B (en) Self-adaptive configuration method of high-performance big data analysis system in cloud computing environment
Jin et al. Training large scale deep neural networks on the intel xeon phi many-core coprocessor
CN103620624A (en) Method and apparatus for locally competitive learning rules leading to sparse connectivity
Chatterjee et al. Towards optimal quantization of neural networks
Xie et al. Energy efficiency enhancement for cnn-based deep mobile sensing
Basheer et al. Alternating layered variational quantum circuits can be classically optimized efficiently using classical shadows
Spallanzani et al. Additive noise annealing and approximation properties of quantized neural networks
Grubic et al. Synchronous multi-gpu deep learning with low-precision communication: An experimental study
Nguyen et al. A low-power, high-accuracy with fully on-chip ternary weight hardware architecture for Deep Spiking Neural Networks
Chung et al. Multi-objective evolutionary architectural pruning of deep convolutional neural networks with weights inheritance
CN113570037A (en) Neural network compression method and device
Cui et al. Deep Bayesian optimization on attributed graphs
Liu et al. SuperPruner: Automatic neural network pruning via super network
KR20210157826A (en) Method for sturcture learning and model compression for deep neural netwrok
CN117634580A (en) Data processing method, training method and related equipment of neural network model
Petschenig et al. Quantized rewiring: hardware-aware training of sparse deep neural networks
Pierro et al. Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity
Verma et al. Clustered network adaptation methodology for the resource constrained platform