Results 101 to 110 of about 5,075,844 (278)

On the Hardware Implementation of Triangle Traversal Algorithms for Graphics Processing [PDF]

, 2010
Current GPU architectures provide impressive processing rates in graphical applications because of their specialized graphics pipeline. However, little attention has been paid to the analysis and study of different hardware architectures to implement ...
Royer del Barrio, Pablo, López Vallejo, Marisa, López Barrio, Carlos Alberto, Ituero Herrero, Pablo   +3 more
core  

Molecular Rectification: Mechanisms, Modulation Strategies, and Device Realization

Advanced Science, EarlyView.
This review focuses on molecular rectification, from its fundamental mechanisms and modulation strategies to device realization. It summarizes the theoretical framework, modulation strategies, fabrication techniques, characterization methods, and modeling and simulation of molecular rectifiers, while discussing challenges and future directions for ...
Junnan Guo, Shufan Song, Wenhui Fang, Jifeng Tang, Wenhao Li, Weikang Wu, Shishen Yan, Hui Li, Lishu Zhang   +8 more
wiley   +1 more source

Electromagnetic scattering and imaging simulation of extremely large-scale sea-ship scene based on GPU parallel technology

Journal of Electronic Science and Technology
Aiming to solve the bottleneck problem of electromagnetic scattering simulation in the scenes of extremely large-scale seas and ships, a high-frequency method by using graphics processing unit (GPU) parallel acceleration technique is proposed.
Cheng-Wei Zhang, Zhi-Qin Zhao, Wei Yang, Li-Lai Zhou, Hai-Yu Zhu   +4 more
doaj   +1 more source

Hardware‐Native Gaussian Sampling With Probabilistic Ising Machines for Modern Communication Paradigms

Advanced Science, EarlyView.
Probabilistic Ising machines are introduced as hardware‐native Gaussian samplers for edge communication workloads. Tunable variance and sampling density enable accurate distributions for Falcon‐class post‐quantum signatures and diffusion‐based semantic communication.
Davi Rodrigues, Andrea Grimaldi, Deborah Volpe, Youwei Bao, Shuhan Yang, Eleonora Grassucci, Giulio Siracusano, Massimo Chiappini, Mario Carpentieri, Hyunsoo Yang, Giovanni Finocchio   +10 more
wiley   +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

Reverberation modeling on graphics processing units (GPUs) [PDF]

Proceedings of Meetings on Acoustics, 2013
Paul Hursky, Ahmad T. Abawi
openaire   +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

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

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Data‐Driven High‐Throughput Volume Fraction Estimation From X‐Ray Diffraction Patterns

Advanced Intelligent Discovery, EarlyView.
Long exposure times and the need for manual evaluation limit the use of X‐ray diffraction in high‐throughput applications. This study presents a data‐driven approach addressing both issues. HiVE (a method for High‐throughput Volume fraction Estimation) performs composition estimation for high‐noise XRD patterns produced using polychromatic emission ...
Hawo H. Höfer, André Orth, Robert Wang, Ben Breitung, Simon Schweidler, Jasmin Aghassi‐Hagmann, Markus Reischl   +6 more
wiley   +1 more source