[go: up one dir, main page]

Bohnstingl et al., 2020 - Google Patents

Accelerating spiking neural networks using memristive crossbar arrays

Bohnstingl et al., 2020

Document ID
12623344189069561011
Author
Bohnstingl T
Pantazi A
Eleftheriou E
Publication year
Publication venue
2020 27th IEEE International Conference on Electronics, Circuits and Systems (ICECS)

External Links

Snippet

Biologically-inspired spiking neural networks (SNNs) hold great promise to perform demanding tasks in an energy and area-efficient manner. Memristive devices organized in a crossbar array can be used to accelerate operations of artificial neural networks (ANNs) …
Continue reading at ieeexplore.ieee.org (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
    • G06N3/06—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
    • G06N3/08—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
    • G06N3/0472—Architectures, e.g. interconnection topology using probabilistic elements, e.g. p-rams, stochastic processors
    • 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
    • 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
    • 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
    • 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
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F7/00—Methods or arrangements for processing data by operating upon the order or content of the data handled
    • G06F7/38—Methods or arrangements for performing computations using exclusively denominational number representation, e.g. using binary, ternary, decimal representation

Similar Documents

Publication Publication Date Title
US10740671B2 (en) Convolutional neural networks using resistive processing unit array
US10956815B2 (en) Killing asymmetric resistive processing units for neural network training
US9779355B1 (en) Back propagation gates and storage capacitor for neural networks
Kan et al. Simple reservoir computing capitalizing on the nonlinear response of materials: theory and physical implementations
CN107924227A (en) Resistance processing unit
Schürmann et al. Edge of chaos computation in mixed-mode vlsi-a hard liquid
Zou et al. Spiking hyperdimensional network: Neuromorphic models integrated with memory-inspired framework
Solomon Analog neuromorphic computing using programmable resistor arrays
US20240394523A1 (en) Sampling artificial neural networks
US20250005341A1 (en) Computing apparatus based on spiking neural network and operating method of computing apparatus
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
Zhang et al. Spiking neural network implementation on fpga for multiclass classification
Yan et al. CQ $^{+} $+ Training: Minimizing Accuracy Loss in Conversion From Convolutional Neural Networks to Spiking Neural Networks
Chen et al. Surrogate gradient scaling for directly training spiking neural networks
Bohnstingl et al. Accelerating spiking neural networks using memristive crossbar arrays
US20220351035A1 (en) Apparatus and method for neural network learning using synapse based on multi element
Nair et al. Direct CMOS implementation of neuromorphic temporal neural networks for sensory processing
Li et al. Binary‐stochasticity‐enabled highly efficient neuromorphic deep learning achieves better‐than‐software accuracy
Saraswat et al. Hardware-friendly synaptic orders and timescales in liquid state machines for speech classification
Vianello et al. Multiple binary oxrams as synapses for convolutional neural networks
Stanojevic et al. File classification based on spiking neural networks
US6490571B1 (en) Method and apparatus for neural networking using semantic attractor architecture
Nepomnyashchiy et al. Method of recurrent neural network hardware implementation
Nowshin Spiking neural network with memristive based computing-in-memory circuits and architecture