Results 161 to 170 of about 16,671 (310)

GPU Acceleration of a Numerical Solver for Particle-Laden Turbulent Flows Using OpenACC

reservedLo scopo di questa tesi magistrale è lo sviluppo e l’ottimizzazione di un codice numerico per la simulazione del trasporto di particelle in flussi turbolenti incomprimibili, sfruttando la potenza computazionale delle moderne architetture GPU. Il
XHEKA, GRESA
core  

Polarization Dynamics in Ferroelectrics: Insights Enabled by Machine Learning Molecular Dynamics

Advanced Science, EarlyView.
Machine learning molecular dynamics is presented as a route to capture polarization switching, domain wall kinetics, topological polar textures, and polar mechanical coupling beyond the limits of conventional atomistic methods. This Perspective surveys recent progress and identifies key methodological directions, including long‐range electrostatics ...
Dongyu Bai, Ri He, Junxian Liu, Liangzhi Kou   +3 more
wiley   +1 more source

Electrode‐Engineered Dual‐Mode Multifunctional Lead‐Free Perovskite Optoelectronic Memristors for Neuromorphic Computing

Advanced Electronic Materials, EarlyView.
A lead‐free perovskite memristive solar cell structure that call emulate both synaptic and neuronal functions controlled by light and electric fields depending on top electrode type. ABSTRACT Memristive devices based on halide perovskites hold strong promise to provide energy‐efficient systems for the Internet of Things (IoT); however, lead (Pb ...
Michalis Loizos, Konstantinos Rogdakis, Konstantinos Chatzimanolis, Katerina Anagnostou, Emmanuel Kymakis   +4 more
wiley   +1 more source

Accelerating an implicit ocean model using CUDA C

Applied Ocean Research
In this study, we developed an ocean model named GPU-IOCASM (GPU-Implicit Ocean Current and Storm Surge Model), which employs the finite difference method with implicit iteration to ensure simulation stability.
Jianbin Xie, Xingru Feng, Tianhai Gao, Changming Dong, Baoshu Yin, Changmao Wu   +5 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

EGA: An Efficient GPU Accelerated Groupby Aggregation Algorithm

Applied Sciences
With the exponential growth of big data, efficient groupby aggregation (GA) has become critical for real-time analytics across industries. GA is a key method for extracting valuable information.
Zhe Wang, Yao Shen, Zhou Lei
doaj   +1 more source

RDMA data transfer and GPU acceleration methods for high-throughput online processing of serial crystallography images. [PDF]

J Synchrotron Radiat, 2020
Ponsard R, Janvier N, Kieffer J, Houzet D, Fristot V.   +4 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

GPU acceleration of a model-based iterative method for Digital Breast Tomosynthesis. [PDF]

Sci Rep, 2020
Cavicchioli R, Hu JC, Loli Piccolomini E, Morotti E, Zanni L.   +4 more
europepmc   +1 more source

Experimental Demonstration of Temporally Aware Fault‐Tolerant Sensor Fusion Using Memristive Associative Learning

Advanced Electronic Materials, EarlyView.
In dynamic driving scenarios, the proposed approach ensures only temporally aligned sensor inputs to make driving decisions, preventing false activations. By enabling selective hardware‐level learning, it achieves fast, reliable responses under noisy conditions.
Kapil Bhardwaj, Ashfak Nehal, Dmitrii Semenov, Roman Sotner, Sayani Majumdar   +4 more
wiley   +1 more source