Results 41 to 50 of about 15,800,744 (205)

Neuromorphic Electronics for Intelligence Everywhere: Emerging Devices, Flexible Platforms, and Scalable System Architectures

Advanced Materials, EarlyView.
The perspective presents an integrated view of neuromorphic technologies, from device physics to real‐time applicability, while highlighting the necessity of full‐stack co‐optimization. By outlining practical hardware‐level strategies to exploit device behavior and mitigate non‐idealities, it shows pathways for building efficient, scalable, and ...
Kapil Bhardwaj, Roshni Satheesh Babu, Yuxin Xia, Eva Bestelink, Radu Sporea, Nikos Hastas, Ioannis Zeimpekis, Dimitra G. Georgiadou, Sayani Majumdar   +8 more
wiley   +1 more source

An Energy-Efficient Digital ReRAM-Crossbar-Based CNN With Bitwise Parallelism

IEEE Journal on Exploratory Solid-State Computational Devices and Circuits, 2017
There is great attention to develop hardware accelerator with better energy efficiency, as well as throughput, than GPUs for convolutional neural network (CNN). The existing solutions have relatively limited parallelism as well as large power consumption
Leibin Ni, Zichuan Liu, Hao Yu, Rajiv V. Joshi   +3 more
doaj   +1 more source

Flexible resistive random access memory devices by using NiOx/GaN microdisk arrays fabricated on graphene films

, 2017
We report flexible resistive random access memory (ReRAM) arrays fabricated by using NiOx/GaN microdisk arrays on graphene films. The ReRAM device was created from discrete GaN microdisk arrays grown on graphene films produced by chemical vapor ...
Yoon, Chansoo, Chansoo Yoon, Lee, Keundong, Yoon, Hosang, Park, Bae Ho, Chung, Kunook, Yi, Gyu-Chul, Bae Ho Park, Jiyoung Yoon, Lee, Sangik, Youngbin Tchoe, Hosang Yoon, Gyu-Chul Yi, Park, Jong-woo, Jong-woo Park, Keundong Lee, Sangik Lee, Kunook Chung, Tchoe, Youngbin, Yoon, Jiyoung   +19 more
core   +1 more source

Demonstration of transfer learning using 14 nm technology analog ReRAM array

Frontiers in Electronics
Analog memory presents a promising solution in the face of the growing demand for energy-efficient artificial intelligence (AI) at the edge. In this study, we demonstrate efficient deep neural network transfer learning utilizing hardware and algorithm co-
Fabia Farlin Athena, Omobayode Fagbohungbe, Nanbo Gong, Malte J. Rasch, Jimmy Penaloza, SoonCheon Seo, Arthur Gasasira, Paul Solomon, Valeria Bragaglia, Steven Consiglio, Hisashi Higuchi, Chanro Park, Kevin Brew, Paul Jamison, Christopher Catano, Iqbal Saraf, Claire Silvestre, Xuefeng Liu, Babar Khan, Nikhil Jain, Steven McDermott, Rick Johnson, I. Estrada-Raygoza, Juntao Li, Tayfun Gokmen, Ning Li, Ruturaj Pujari, Fabio Carta, Hiroyuki Miyazoe, Martin M. Frank, Antonio La Porta, Devi Koty, Qingyun Yang, Robert D. Clark, Kandabara Tapily, Cory Wajda, Aelan Mosden, Jeff Shearer, Andrew Metz, Sean Teehan, Nicole Saulnier, Bert Offrein, Takaaki Tsunomura, Gert Leusink, Vijay Narayanan, Takashi Ando   +45 more
doaj   +1 more source

Morphology‐Defined Entropy Streams for Random‐Bit Generation and Probabilistic Sampling in Memristor Arrays

Advanced Science, EarlyView.
Au nanoisland morphology defines stochastic high‐resistance‐state transport in Ag/TiO2 memristor arrays, transforming nanoscale structural disorder into a tunable physical entropy resource. Morphology‐controlled field hotspots and reset‐modulated nanogaps generate statistically validated random‐bit streams, which further serve as device‐derived ...
Hyunjun Kim, Da Ye Kim, Noah Jang, Yongho Lee, Byunghun Song, June Soo Kim   +5 more
wiley   +1 more source

Total Ionizing Dose Test Report for the MB85AS4MT ReRAM [PDF]

, 2017
This is a Total Ionizing Dose (TID) test report for the Fujitsu Semiconductor MB85AS4MT Resistive Random Access Memory (ReRAM)
LaBel, Kenneth, Chen, Dakai, Wilcox, Edward   +2 more
core  

Low‐Power Control Of Resistance Switching Transitions in First‐Order Memristors

Advanced Electronic Materials, EarlyView.
Joule losses are a serious concern in modern integrated circuit design. In this regard, minimizing the energy necessary for programming memristors should be handled with care. This manuscript presents an optimal control framework, allowing to derive energy‐efficient programming voltage protocols for resistance switching devices. Following this approach,
Valeriy A. Slipko, Alon Ascoli, Fernando Corinto, Yuriy V. Pershin   +3 more
wiley   +1 more source

Characterization and Modeling of Multilevel Analog ReRAM Synapses in the Sky130 Process

IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Nonvolatile memory devices play a key role in enabling energy-efficient computing. Among them, analog nonvolatile memories such as resistive random access memory (ReRAM) offer high density and low power compared to conventional digital memories. However,
Irem Didin, Carl Brando, Ching-Yi Lin, Sahil Shah   +3 more
doaj   +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

Lead-free Cs2CuBiCl6 double perovskite for ReRAM: Electrode-dependent resistive switching and performance analysis

Memories - Materials, Devices, Circuits and Systems
Resistive Random-Access Memory (ReRAM) is a promising technology for next-generation non-volatile memory and neuromorphic computing due to its scalability, low power consumption, and rapid switching.
Rajib Sutradhar, Chinmaya Kumar Pradhan, Himangshu Jyoti Gogoi, Pukhrambam Puspa Devi, Kuldeep Gogoi, Dipjyoti Das   +5 more
doaj   +1 more source