[go: up one dir, main page]

Hu et al., 2014 - Google Patents

Memristor crossbar-based neuromorphic computing system: A case study

Hu et al., 2014

Document ID
678964691175647532
Author
Hu M
Li H
Chen Y
Wu Q
Rose G
Linderman R
Publication year
Publication venue
IEEE transactions on neural networks and learning systems

External Links

Snippet

By mimicking the highly parallel biological systems, neuromorphic hardware provides the capability of information processing within a compact and energy-efficient platform. However, traditional Von Neumann architecture and the limited signal connections have …
Continue reading at ieeexplore.ieee.org (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
    • G06N3/04Architectures, e.g. interconnection topology
    • G06N3/0454Architectures, e.g. interconnection topology using a combination of multiple neural nets
    • 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
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/06Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/067Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using optical 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
    • G06N3/04Architectures, e.g. interconnection topology
    • G06N3/049Temporal neural nets, e.g. delay elements, oscillating neurons, pulsed inputs
    • 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
    • G06N3/10Simulation on general purpose computers

Similar Documents

Publication Publication Date Title
Hu et al. Memristor crossbar-based neuromorphic computing system: A case study
US9715655B2 (en) Method and apparatus for performing close-loop programming of resistive memory devices in crossbar array based hardware circuits and systems
Zhang et al. Memristor-based circuit design for multilayer neural networks
Gokmen et al. Acceleration of deep neural network training with resistive cross-point devices: Design considerations
Wijesinghe et al. An all-memristor deep spiking neural computing system: A step toward realizing the low-power stochastic brain
Hu et al. Hardware realization of BSB recall function using memristor crossbar arrays
Hu et al. Memristor crossbar based hardware realization of BSB recall function
Gokmen et al. Training LSTM networks with resistive cross-point devices
Hu et al. BSB training scheme implementation on memristor-based circuit
Dong et al. Design and implementation of a flexible neuromorphic computing system for affective communication via memristive circuits
Fouda et al. Mask technique for fast and efficient training of binary resistive crossbar arrays
Yakopcic et al. Energy efficient perceptron pattern recognition using segmented memristor crossbar arrays
Qin et al. Design of high robustness BNN inference accelerator based on binary memristors
Yang et al. Security of neuromorphic computing: thwarting learning attacks using memristor's obsolescence effect
Yang et al. Thwarting replication attack against memristor-based neuromorphic computing system
Song et al. ITT-RNA: Imperfection tolerable training for RRAM-crossbar-based deep neural-network accelerator
Fu et al. Memristor-based neuromorphic hardware improvement for privacy-preserving ANN
Bhattacharjee et al. Efficiency-driven hardware optimization for adversarially robust neural networks
Zhang et al. Neural network training with stochastic hardware models and software abstractions
Pagliarini et al. A probabilistic synapse with strained MTJs for spiking neural networks
Ji et al. MLG-NCS: Multimodal local–global neuromorphic computing system for affective video content analysis
Quan et al. Training-free stuck-at fault mitigation for ReRAM-based deep learning accelerators
Wang et al. Reconfigurable neuromorphic crossbars based on titanium oxide memristors
Yakopcic et al. Self‐biasing memristor crossbar used for string matching and ternary content‐addressable memory implementation
US12367385B2 (en) Unsupervised learning of memristor crossbar neuromorphic processing systems