[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Doevenspeck et al., 2021 - Google Patents

OxRRAM-based analog in-memory computing for deep neural network inference: A conductance variability study

Doevenspeck et al., 2021

Document ID
7329584276201591538
Author
Doevenspeck J
Degraeve R
Fantini A
Cosemans S
Mallik A
Debacker P
Verkest D
Lauwereins R
Dehaene W
Publication year
Publication venue
IEEE Transactions on Electron Devices

External Links

Snippet

Analog in-memory compute (AiMC) is a promising approach to efficiently process deep neural networks (DNNs). Due to its small size and nonvolatility, oxide resistive random access memory (OxRRAM) is being considered as a memory device to map neural network …
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
    • G11INFORMATION STORAGE
    • G11CSTATIC STORES
    • G11C13/00Digital stores characterised by the use of storage elements not covered by groups G11C11/00, G11C23/00 - G11C25/00
    • G11C13/0002Digital stores characterised by the use of storage elements not covered by groups G11C11/00, G11C23/00 - G11C25/00 using resistance random access memory [RRAM] elements
    • G11C13/0021Auxiliary circuits
    • G11C13/0069Writing or programming circuits or methods
    • GPHYSICS
    • G11INFORMATION STORAGE
    • G11CSTATIC STORES
    • G11C13/00Digital stores characterised by the use of storage elements not covered by groups G11C11/00, G11C23/00 - G11C25/00
    • G11C13/0002Digital stores characterised by the use of storage elements not covered by groups G11C11/00, G11C23/00 - G11C25/00 using resistance random access memory [RRAM] elements
    • G11C13/0021Auxiliary circuits
    • G11C13/0064Verifying circuits or methods
    • 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
    • GPHYSICS
    • G11INFORMATION STORAGE
    • G11CSTATIC STORES
    • G11C13/00Digital stores characterised by the use of storage elements not covered by groups G11C11/00, G11C23/00 - G11C25/00
    • G11C13/0002Digital stores characterised by the use of storage elements not covered by groups G11C11/00, G11C23/00 - G11C25/00 using resistance random access memory [RRAM] elements
    • G11C13/0021Auxiliary circuits
    • G11C13/0061Timing circuits or methods
    • GPHYSICS
    • G11INFORMATION STORAGE
    • G11CSTATIC STORES
    • G11C13/00Digital stores characterised by the use of storage elements not covered by groups G11C11/00, G11C23/00 - G11C25/00
    • G11C13/0002Digital stores characterised by the use of storage elements not covered by groups G11C11/00, G11C23/00 - G11C25/00 using resistance random access memory [RRAM] elements
    • G11C13/0021Auxiliary circuits
    • G11C13/004Reading or sensing circuits or methods
    • 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
    • GPHYSICS
    • G11INFORMATION STORAGE
    • G11CSTATIC STORES
    • G11C13/00Digital stores characterised by the use of storage elements not covered by groups G11C11/00, G11C23/00 - G11C25/00
    • G11C13/0002Digital stores characterised by the use of storage elements not covered by groups G11C11/00, G11C23/00 - G11C25/00 using resistance random access memory [RRAM] elements
    • G11C13/0007Digital stores characterised by the use of storage elements not covered by groups G11C11/00, G11C23/00 - G11C25/00 using resistance random access memory [RRAM] elements comprising metal oxide memory material, e.g. perovskites
    • GPHYSICS
    • G11INFORMATION STORAGE
    • G11CSTATIC STORES
    • G11C11/00Digital stores characterised by the use of particular electric or magnetic storage elements; Storage elements therefor
    • G11C11/56Digital stores characterised by the use of particular electric or magnetic storage elements; Storage elements therefor using storage elements with more than two stable states represented by steps, e.g. of voltage, current, phase, frequency
    • G11C11/5685Digital stores characterised by the use of particular electric or magnetic storage elements; Storage elements therefor using storage elements with more than two stable states represented by steps, e.g. of voltage, current, phase, frequency using storage elements comprising metal oxide memory material, e.g. perovskites
    • 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
    • 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
    • G11INFORMATION STORAGE
    • G11CSTATIC STORES
    • G11C11/00Digital stores characterised by the use of particular electric or magnetic storage elements; Storage elements therefor
    • G11C11/02Digital stores characterised by the use of particular electric or magnetic storage elements; Storage elements therefor using magnetic elements
    • 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
    • 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

Similar Documents

Publication Publication Date Title
Nandakumar et al. Mixed-precision deep learning based on computational memory
Wijesinghe et al. An all-memristor deep spiking neural computing system: A step toward realizing the low-power stochastic brain
Nandakumar et al. Mixed-precision architecture based on computational memory for training deep neural networks
Xia et al. Fault-tolerant training with on-line fault detection for RRAM-based neural computing systems
US9715655B2 (en) Method and apparatus for performing close-loop programming of resistive memory devices in crossbar array based hardware circuits and systems
Roy et al. TxSim: Modeling training of deep neural networks on resistive crossbar systems
Xia et al. Fault-tolerant training enabled by on-line fault detection for RRAM-based neural computing systems
Kadetotad et al. Parallel architecture with resistive crosspoint array for dictionary learning acceleration
Mohanty et al. Random sparse adaptation for accurate inference with inaccurate multi-level RRAM arrays
Oh et al. Drift-enhanced unsupervised learning of handwritten digits in spiking neural network with PCM synapses
Doevenspeck et al. OxRRAM-based analog in-memory computing for deep neural network inference: A conductance variability study
Shim et al. Investigation of read disturb and bipolar read scheme on multilevel RRAM-based deep learning inference engine
Shim et al. Impact of read disturb on multilevel RRAM based inference engine: Experiments and model prediction
Nandakumar et al. Mixed-precision training of deep neural networks using computational memory
Pedretti et al. Conductance variations and their impact on the precision of in-memory computing with resistive switching memory (RRAM)
Fritscher et al. Simulating large neural networks embedding MLC RRAM as weight storage considering device variations
Lee et al. Neuromorphic computing using random synaptic feedback weights for error backpropagation in NAND flash memory-based synaptic devices
Hassan et al. Hybrid spiking-based multi-layered self-learning neuromorphic system based on memristor crossbar arrays
Babu et al. Stochastic learning in deep neural networks based on nanoscale PCMO device characteristics
Han et al. Efficient discrete temporal coding spike-driven in-memory computing macro for deep neural network based on nonvolatile memory
Saxena High LRS-resistance CMOS memristive synapses for energy-efficient neuromorphic SoCs
Xiao et al. Device variation-aware adaptive quantization for MRAM-based accurate in-memory computing without on-chip training
Cao et al. Performance analysis of convolutional neural network using multi-level memristor crossbar for edge computing
Zhao et al. A 0.02% accuracy loss voltage-mode parallel sensing scheme for RRAM-based XNOR-net application
Liu et al. AIDX: Adaptive inference scheme to mitigate state-drift in memristive VMM accelerators