Results 191 to 200 of about 6,113,843 (284)

Electromagnetic waveform processing through time-modulated thin sheets. [PDF]

Sci Rep
Kadlec P, Bednarský V, Antonini G, Joler M, Štumpf M.   +4 more
europepmc   +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

Feedback neurons based on perovskite memristor with nickel single-atom engineered reduced graphene oxide cathode. [PDF]

Nat Commun
Li QX, Li HX, Sun T, Jiang MH, Leng YB, Lv ZY, Zhou Y, Zhou Y, Wong WY, Han ST.   +9 more
europepmc   +1 more source

Ultra‐Low Power Consumption and Highly Durability in Sm:HfO2 Thin Film Ferroelectric Memristor for Edge Detection

Advanced Electronic Materials, EarlyView.
ABSTRACT With the continuous development of computer image processing, developing efficient and low‐power computing devices has become a key challenge. Memristors have integrated in‐situ storage and computing capabilities, making them an ideal choice for low‐power image processing computing architectures. However, current memristors are confronted with
Tengyu Li, YongRui Wang, Weifeng Zhang, Jikang Xu, Xiaobing Yan   +4 more
wiley   +1 more source

Group-Patch Joint Compression: Compressing Dynamic B₀ and Static RF Spatial Modulations Across k-Space Subregion Groups for Highly Accelerated MRI. [PDF]

Magn Reson Med
Tian R, Scheffler K.
europepmc   +1 more source

People Counting and Positioning Using Low‐Resolution Infrared Images for FeFET‐Based In‐Memory Computing

Advanced Electronic Materials, EarlyView.
In this work, low‐resolution infrared imaging is combined with a 28 nm FeFET IMC architecture to enable compact, energy‐efficient edge inference. MLC FeFET devices are experimentally characterized, and controlled multi‐level current accumulation is validated at crossbar array level.
Alptekin Vardar, Li Zhang, Alexander Rehmer, Marc Lupp, Subhashree Baskaran, Osama Abdelaal, Franz Müller, Nandakishor Yadav, Bing Li, Thomas Kämpfe   +9 more
wiley   +1 more source

Perception, Synaptic Plasticity, and Spiking Neuron Function Enabled by a 2D Ferroelectric NbOBr₂ for Neuromorphic Vision. [PDF]

Adv Mater
Yu Z, Zhao Z, Han Q, Wang Q, Zeng T, Zeng Y, Xing Y, Fang B, Lv W, Zhao C, Cheng Y, Geng L, Liu Z, Zeng Z.   +13 more
europepmc   +1 more source

Efficient In‐Hardware Matrix–Vector Multiplication and Addition Exploiting Bilinearity of Schottky Barrier Transistors Processed on Industrial FDSOI

Advanced Electronic Materials, EarlyView.
ABSTRACT Machine learning and Artificial Intelligence (AI) tasks have stretched traditional hardware to its limits. In‐hardware computation is a novel approach that aims to run complex operations, such as matrix–vector multiplication, directly at the device level for increased efficiency.
Juan P. Martinez, Niladri Bhattacharjee, Yuxuan He, Giulio Galderisi, Brojogopal Sapui, Sara Mannaa, Ian O'Connor, Violetta Sessi, Peter Javorka, Thomas Mikolajick, Jens Trommer   +10 more
wiley   +1 more source

Spectrally Partitioned All-Optical Memristors for Highly Linear Neuromorphic Vision Systems. [PDF]

Nanomicro Lett
Zhang J, Sun B, Wang S, Zhou G, Sun L, Cao Z, Gao K, Li X, Chen X, Shao J.   +9 more
europepmc   +1 more source

On the Role of Preprocessing and Memristor Dynamics in Reservoir Computing for Image Classification

Advanced Electronic Materials, EarlyView.
ABSTRACT Reservoir computing (RC) is an emerging recurrent neural network architecture that has attracted growing attention for its low training cost and modest hardware requirements. Memristor‐based circuits are particularly promising for RC, as their intrinsic dynamics can reduce network size and parameter overhead in tasks such as time‐series ...
Rishona Daniels, Duna Wattad, Ronny Ronen, David Saad, Shahar Kvatinsky   +4 more
wiley   +1 more source