Xia et al., 2021 - Google Patents
Fully dynamic inference with deep neural networksXia et al., 2021
View PDF- Document ID
- 1563526910025213152
- Author
- Xia W
- Yin H
- Dai X
- Jha N
- Publication year
- Publication venue
- IEEE Transactions on Emerging Topics in Computing
External Links
Snippet
Modern deep neural networks are powerful and widely applicable models that extract task- relevant information through multi-level abstraction. Their cross-domain success, however, is often achieved at the expense of computational cost, high memory bandwidth, and long …
- 230000001537 neural 0 title abstract description 21
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
- G06N3/04—Architectures, e.g. interconnection topology
-
- 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
-
- 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
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- 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
-
- 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
- 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
- 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
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
-
- 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
- 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
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F15/00—Digital computers in general; Data processing equipment in general
- G06F15/18—Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Xia et al. | Fully dynamic inference with deep neural networks | |
Shiri et al. | A comprehensive overview and comparative analysis on deep learning models: CNN, RNN, LSTM, GRU | |
CN112561027B (en) | Neural network architecture search method, image processing method, device and storage medium | |
Gomez et al. | Learning sparse networks using targeted dropout | |
CN109522942B (en) | An image classification method, device, terminal device and storage medium | |
Maaløe et al. | Auxiliary deep generative models | |
Li et al. | DS-Net++: Dynamic weight slicing for efficient inference in CNNs and vision transformers | |
CN111507993A (en) | Image segmentation method and device based on generation countermeasure network and storage medium | |
CN113705769A (en) | Neural network training method and device | |
Singh et al. | Shunt connection: An intelligent skipping of contiguous blocks for optimizing MobileNet-V2 | |
US10776691B1 (en) | System and method for optimizing indirect encodings in the learning of mappings | |
Kim et al. | Exploring temporal information dynamics in spiking neural networks | |
CN103914711B (en) | A kind of improved very fast learning device and its method for classifying modes | |
CN111260020A (en) | Method and device for calculating convolutional neural network | |
Huang et al. | LTNN: A layerwise tensorized compression of multilayer neural network | |
AU2022281121C1 (en) | Generating neural network outputs by cross attention of query embeddings over a set of latent embeddings | |
CN114925320A (en) | Data processing method and related device | |
Du et al. | Efficient network construction through structural plasticity | |
CN115018039A (en) | Neural network distillation method, target detection method and device | |
Han et al. | Improving low-latency predictions in multi-exit neural networks via block-dependent losses | |
Wistuba | Bayesian optimization combined with incremental evaluation for neural network architecture optimization | |
Veerabadran et al. | Adaptive recurrent vision performs zero-shot computation scaling to unseen difficulty levels | |
Wang et al. | Codinet: Path distribution modeling with consistency and diversity for dynamic routing | |
CN116630816B (en) | SAR target recognition method, device, equipment and medium based on prototype comparison learning | |
Li et al. | Denoising diffusion probabilistic models and transfer learning for citrus disease diagnosis |