Results 11 to 20 of about 4,276 (120)

Update Disturbance-Resilient Analog ReRAM Crossbar Arrays for In-Memory Deep Learning Accelerators. [PDF]

Adv Sci (Weinh)
Resistive memory (ReRAM) technologies with crossbar array architectures hold significant potential for analog AI accelerator hardware, enabling both in‐memory inference and training.
Choi W, Stecconi T, Falcone DF, Galetta M, Clerico V, Zaccaria E, Ram MS, Porta A, Horst F, Jubin D, Senger M, Sousa M, Reidt S, Heller R, Linares-Barranco B, Bragaglia V, Offrein BJ.   +16 more
europepmc   +3 more sources

α-CsPbI₃ Quantum Dots ReRAM with High Air Stability Working by Valance Change Filamentary Mechanism. [PDF]

Small Methods
Making the α‐CsPbI3 into QDs and applying it to the device with inert Au as the top electrode result in the valence change mechanism‐based devices. The large surface‐to‐volume ratio makes an abundant amount of iodine vacancies and facile migration of vacancies allows the device to work by valence change mechanism.
Lee DE, Im IH, Baek JH, Kwak KJ, Kim SJ, Lee TH, Kim JY, Jang HW.   +7 more
europepmc   +2 more sources

VO_x-Based Non-Volatile Radio-Frequency Switches for Reconfigurable Filter. [PDF]

Adv Sci (Weinh)
Vanadium oxide‐based memristor devices achieve high performance for next‐generation non‐volatile memory and RF switches. These devices demonstrate a cutoff frequency of ≈4.5 THz, low insertion loss (<0.46 dB), and high isolation (>20 dB) from 0.1 to 20 GHz with stable operation extended to 67 GHz.
Seo D, Kim D, Ryu J, Pyo C, Lee S, Yoon TS, Kim M.   +6 more
europepmc   +2 more sources

Non-Volatile Resistive Switching in Nanoscaled Elemental Tellurium by Vapor Transport Deposition on Gold. [PDF]

Adv Sci (Weinh)
In this study, the resistive switching behavior of nanoscaled tellurium films using conductive atomic force microscopy is revealed, offering valuable insights into their memristive functionality. It is demonstrated that this memristive behavior is maintained in cross‐point devices and can be further enhanced by using gold as the substrate material ...
Ghomi S, Martella C, Lee Y, Chang PH, Targa P, Serafini A, Codegoni D, Massetti C, Gharedaghi S, Lamperti A, Grazianetti C, Akinwande D, Molle A.   +12 more
europepmc   +2 more sources

Resistive-RAM-Based In-Memory Computing for Neural Network: A Review

Electronics, 2022
Processing-in-memory (PIM) is a promising architecture to design various types of neural network accelerators as it ensures the efficiency of computation together with Resistive Random Access Memory (ReRAM).
Weijian Chen, Zhi Qi, Zahid Akhtar, Kamran Siddique   +3 more
semanticscholar   +1 more source

Accelerated Addition in Resistive RAM Array Using Parallel-Friendly Majority Gates

IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2021
To overcome the “von Neumann bottleneck,” methods to compute in memory are being researched in many emerging memory technologies, including resistive RAMs (ReRAMs).
J. Reuben, Stefan Pechmann
semanticscholar   +1 more source

Carry-free Addition in Resistive RAM Array: n-bit Addition in 22 Memory Cycles

IEEE Computer Society Annual Symposium on VLSI, 2021
The movement of data between processing and memory units, often referred to as the ‘von Neumann bottleneck’ is the main reason for the degraded performance of contemporary computing systems.
J. Reuben, D. Fey
semanticscholar   +1 more source

Effect of aluminum interfacial layer in a niobium oxide based resistive RAM

Solid State Electronics Letters, 2019
Resistive RAM (Random Access Memory) has good scalability with high switching speed and low operating voltage making it one of the promising emerging nonvolatile memory technologies.
V. Manjunath, A. Rush, A. Barua, R. Jha
semanticscholar   +1 more source

Emerging Memory and Device Technologies for Hardware‐Accelerated Model Training and Inference

Advanced Electronic Materials, EarlyView.
This review investigates the suitability of various emerging memory technologies as compute‐in‐memory hardware for artificial intelligence (AI) applications. Distinct requirements for training‐ and inference‐centric computing are discussed, spanning device physics, materials, and system integration.
Yoonho Cho, Dae‐won Kim, Yujin Kim, Jung‐Heum Na, Yuwon Jeong, Dong‐Yeop Lee, Shinhyun Choi   +6 more
wiley   +1 more source

Flexible Memory: Progress, Challenges, and Opportunities

Advanced Intelligent Discovery, EarlyView.
Flexible memory technology is crucial for flexible electronics integration. This review covers its historical evolution, evaluates rigid systems, proposes a flexible memory framework based on multiple mechanisms, stresses material design's role, presents a coupling model for performance optimization, and points out future directions.
Ruizhi Yuan, Sen Chen, Minghui Guo, Ziliang Cui, Yujie Du, Jing Liu   +5 more
wiley   +1 more source