Nothing Special   »   [go: up one dir, main page]

Bai et al., 2024 - Google Patents

The game-changing memristive technology for next-gen AI/ML hardware

Bai et al., 2024

Document ID
8326888674899038172
Author
Bai K
Lombardi J
Thiem C
McDonald N
Publication year
Publication venue
Disruptive Technologies in Information Sciences VIII

External Links

Snippet

Neuromorphic computing is of high importance in Artificial Intelligence (AI) and Machine Learning (ML) to sidestep challenges inherent to neural-inspired computations in modern computing systems. Throughout the development history of neuromorphic computing …
Continue reading at www.spiedigitallibrary.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
    • G11—INFORMATION STORAGE
    • G11C—STATIC STORES
    • G11C13/00—Digital stores characterised by the use of storage elements not covered by groups G11C11/00, G11C23/00 - G11C25/00
    • G11C13/0002—Digital 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
    • G11C13/0009—RRAM elements whose operation depends upon chemical change
    • G11C13/0014—RRAM elements whose operation depends upon chemical change comprising cells based on organic memory material
    • G11C13/0016—RRAM elements whose operation depends upon chemical change comprising cells based on organic memory material comprising polymers
    • G—PHYSICS
    • G11—INFORMATION STORAGE
    • G11C—STATIC STORES
    • G11C13/00—Digital stores characterised by the use of storage elements not covered by groups G11C11/00, G11C23/00 - G11C25/00
    • G11C13/0002—Digital 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
    • G11C13/0021—Auxiliary circuits
    • G—PHYSICS
    • G11—INFORMATION STORAGE
    • G11C—STATIC STORES
    • G11C11/00—Digital stores characterised by the use of particular electric or magnetic storage elements; Storage elements therefor
    • G11C11/02—Digital stores characterised by the use of particular electric or magnetic storage elements; Storage elements therefor using magnetic elements
    • G—PHYSICS
    • G11—INFORMATION STORAGE
    • G11C—STATIC STORES
    • G11C11/00—Digital stores characterised by the use of particular electric or magnetic storage elements; Storage elements therefor
    • G11C11/56—Digital 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
    • 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
    • G—PHYSICS
    • G11—INFORMATION STORAGE
    • G11C—STATIC STORES
    • G11C15/00—Digital stores in which information comprising one or more characteristic parts is written into the store and in which information is read-out by searching for one or more of these characteristic parts, i.e. associative or content-addressed stores
    • G11C15/04—Digital stores in which information comprising one or more characteristic parts is written into the store and in which information is read-out by searching for one or more of these characteristic parts, i.e. associative or content-addressed stores using semiconductor elements
    • 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
    • G—PHYSICS
    • G11—INFORMATION STORAGE
    • G11C—STATIC STORES
    • G11C2213/00—Indexing scheme relating to G11C13/00 for features not covered by this group
    • 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

Similar Documents

Publication Publication Date Title
Sebastian et al. Memory devices and applications for in-memory computing
Chen et al. CMOS-integrated memristive non-volatile computing-in-memory for AI edge processors
Wang et al. Resistive switching materials for information processing
Ni et al. Ferroelectric ternary content-addressable memory for one-shot learning
Pedretti et al. Tree-based machine learning performed in-memory with memristive analog CAM
Yu Neuro-inspired computing with emerging nonvolatile memorys
US20200311512A1 (en) Realization of binary neural networks in nand memory arrays
Luo et al. Accelerating deep neural network in-situ training with non-volatile and volatile memory based hybrid precision synapses
Hung et al. Challenges and trends indeveloping nonvolatile memory-enabled computing chips for intelligent edge devices
Sun et al. A full spectrum of computing-in-memory technologies
Gebregiorgis et al. Tutorial on memristor-based computing for smart edge applications
Rzeszut et al. Multi-state MRAM cells for hardware neuromorphic computing
Yan et al. iCELIA: A full-stack framework for STT-MRAM-based deep learning acceleration
Kingra et al. SLIM: simultaneous logic-in-memory computing exploiting bilayer analog OxRAM devices
KR102605890B1 (en) Multi-level ultra-low power inference engine accelerator
US11170852B1 (en) Cross-bar arrays having steering element with diode
Amrouch et al. Towards reliable in-memory computing: From emerging devices to post-von-neumann architectures
Lepri et al. In-memory computing for machine learning and deep learning
Yan et al. On the reliability of computing-in-memory accelerators for deep neural networks
Yadav et al. Analyzing fault tolerance behaviour in memristor-based crossbar for neuromorphic applications
Rai et al. Perspectives on emerging computation-in-memory paradigms
Youn et al. Programmable threshold logic implementations in a memristor crossbar array
Reis et al. In-memory computing accelerators for emerging learning paradigms
Zolfagharinejad et al. Brain-inspired computing systems: a systematic literature review
Alam et al. Cryogenic in-memory matrix-vector multiplication using ferroelectric superconducting quantum interference device (FE-SQUID)