[go: up one dir, main page]

Klachko et al., 2019 - Google Patents

Improving noise tolerance of mixed-signal neural networks

Klachko et al., 2019

View PDF
Document ID
10203644520271519222
Author
Klachko M
Mahmoodi M
Strukov D
Publication year
Publication venue
2019 International Joint Conference on Neural Networks (IJCNN)

External Links

Snippet

Mixed-signal hardware accelerators for deep learning achieve orders of magnitude better power efficiency than their digital counterparts. In the ultra-low power consumption regime, limited signal precision inherent to analog computation becomes a challenge. We perform a …
Continue reading at arxiv.org (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
    • G06N3/08Learning 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
    • G06N3/0472Architectures, e.g. interconnection topology using probabilistic elements, e.g. p-rams, stochastic processors
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/12Computer systems based on biological models using genetic models
    • G06N3/126Genetic algorithms, i.e. information processing using digital simulations of the genetic system
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50Computer-aided design
    • G06F17/5009Computer-aided design using simulation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/04Inference methods or devices
    • 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
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F7/00Methods or arrangements for processing data by operating upon the order or content of the data handled
    • G06F7/58Random or pseudo-random number generators
    • 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

Similar Documents

Publication Publication Date Title
Klachko et al. Improving noise tolerance of mixed-signal neural networks
Ambrogio et al. Equivalent-accuracy accelerated neural-network training using analogue memory
Kendall et al. Training end-to-end analog neural networks with equilibrium propagation
US11915128B2 (en) Neural network circuit device, neural network processing method, and neural network execution program
Shrestha et al. In-hardware learning of multilayer spiking neural networks on a neuromorphic processor
WO2023143707A1 (en) Training a neural network to perform a machine learning task
Zhang et al. An in-memory-computing DNN achieving 700 TOPS/W and 6 TOPS/mm 2 in 130-nm CMOS
Lou et al. A mixed signal architecture for convolutional neural networks
US20240202513A1 (en) Compact CMOS Spiking Neuron Circuit that works with an Analog Memory-Based Synaptic Array
Harikrishnan et al. Handwritten digit recognition with feed-forward multi-layer perceptron and convolutional neural network architectures
Song et al. Xpikeformer: Hybrid analog-digital hardware acceleration for spiking transformers
Zhang et al. Memristive circuit design of quantized convolutional auto-encoder
KR102857194B1 (en) Apparatus for calculating circuit euqations of processing elements using neural network and method for controlling the same
Li et al. Rethinking residual connection in training large-scale spiking neural networks
Zhang et al. Statistical computing framework and demonstration for in-memory computing systems
Tang et al. Design of highly-accurate and hardware-efficient spiking neural networks
Rinkus A neural model of episodic and semantic spatiotemporal memory
Klachko Hardware Aware Training of Mixed Signal Neural Networks
Le et al. CIMulator: a comprehensive simulation platform for computing-in-memory circuit macros with low bit-width and real memory materials
Huhle et al. Reproduction of AdEx dynamics on neuromorphic hardware through data embedding and simulation-based inference
US11989653B2 (en) Pseudo-rounding in artificial neural networks
CN112241782B (en) Neural programming interpreter with modeling primitives
Salehinejad Energy Models for Pruning Neural Networks
Bettayeb Efficient Hardware Implementation of AI Algorithms for Image Recognition
Zhang Hardware-aware Training for In-memory Computing Systems