Nowshin, 2021 - Google Patents
Spiking neural network with memristive based computing-in-memory circuits and architectureNowshin, 2021
View PDF- Document ID
- 14426601567069561147
- Author
- Nowshin F
- Publication year
External Links
Snippet
In recent years neuromorphic computing systems have achieved a lot of success due to its ability to process data much faster and using much less power compared to traditional Von Neumann computing architectures. There are two main types of Artificial Neural Networks …
- 230000001537 neural 0 title abstract description 71
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
- G06N3/06—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
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/04—Architectures, e.g. interconnection topology
-
- 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
- G06N3/12—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
- 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
- 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
- G06K9/6251—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
-
- 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
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F7/00—Methods or arrangements for processing data by operating upon the order or content of the data handled
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Wang et al. | Integration and co-design of memristive devices and algorithms for artificial intelligence | |
US11501130B2 (en) | Neural network hardware accelerator architectures and operating method thereof | |
Yu et al. | An overview of neuromorphic computing for artificial intelligence enabled hardware-based hopfield neural network | |
Li et al. | Long short-term memory networks in memristor crossbar arrays | |
Thakur et al. | Large-scale neuromorphic spiking array processors: A quest to mimic the brain | |
Yao et al. | Fully hardware-implemented memristor convolutional neural network | |
Woźniak et al. | Deep learning incorporating biologically inspired neural dynamics and in-memory computing | |
Roy et al. | Towards spike-based machine intelligence with neuromorphic computing | |
Shi et al. | Neuroinspired unsupervised learning and pruning with subquantum CBRAM arrays | |
Yu | Neuro-inspired computing with emerging nonvolatile memorys | |
He et al. | A discrete memristive neural network and its application for character recognition | |
US20200356344A1 (en) | Bipolar all-memristor circuit for in-memory computing | |
Schuller et al. | Neuromorphic computing–from materials research to systems architecture roundtable | |
Kang et al. | Deep in-memory architectures in SRAM: An analog approach to approximate computing | |
Wang et al. | Research progress in architecture and application of RRAM with computing-in-memory | |
Zhang et al. | Memristive quantized neural networks: A novel approach to accelerate deep learning on-chip | |
Roy et al. | Scaling deep spiking neural networks with binary stochastic activations | |
JP2023526915A (en) | Efficient Tile Mapping for Rowwise Convolutional Neural Network Mapping for Analog Artificial Intelligence Network Inference | |
Bai et al. | Deep-DFR: A memristive deep delayed feedback reservoir computing system with hybrid neural network topology | |
Nowshin et al. | Recent advances in reservoir computing with a focus on electronic reservoirs | |
Cho et al. | An on-chip learning neuromorphic autoencoder with current-mode transposable memory read and virtual lookup table | |
Zhang et al. | Memristive fuzzy deep learning systems | |
Bailey et al. | Development of a short-term to long-term supervised spiking neural network processor | |
Nowshin | Spiking neural network with memristive based computing-in-memory circuits and architecture | |
Bala et al. | Learning method for ex-situ training of memristor crossbar based multi-layer neural network |