Results 61 to 70 of about 1,831,585 (228)

Neuromorphic Devices and Computing for Sensing, Memory, and Control

Advanced Science, EarlyView.
This review introduces neuromorphic devices made from diverse materials. These devices mimic neuronal functions and architectures and, when integrated with artificial or biological computing, can form closed loops with neurons for pressure, optical, acoustic, and biochemical sensing and modulation.
Zhengguang Zhu, Nicholas Schaffer, Xiao Yang   +2 more
wiley   +1 more source

PAL – parallel active learning for machine-learned potentials† [PDF]

Digital Discovery
Constructing datasets representative of the target domain is essential for training effective machine learning models. Active learning (AL) is a promising method that iteratively extends training data to enhance model performance while minimizing data ...
Chen Zhou, Marlen Neubert, Yuri Koide, Yumeng Zhang, Van-Quan Vuong, Tobias Schlöder, Stefanie Dehnen, Pascal Friederich   +7 more
doaj   +1 more source

Organic Electrochemical Transistors for Neuromorphic Bioelectronics: Materials and Manufacturing Technologies

Advanced Science, EarlyView.
Organic electrochemical transistors bridge biological ionic signaling and neuromorphic computation through volumetric electrochemical gating. This review connects Organic mixed ionic–electronic conductor materials, device architectures, and manufacturing technologies to show how geometry, ion transport, and fabrication strategies program plasticity ...
Hyunjin Lee, Jihyang Park, Xinyi Wang, Yueqi Deng, Jiashu Ren, Tao Zhou   +5 more
wiley   +1 more source

APEX: Asynchronous Parallel CPU-GPU Execution for Online LLM Inference on Constrained GPUs

2026 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
Deploying large language models (LLMs) for online inference is often constrained by limited GPU memory, particularly due to the growing KV cache during auto-regressive decoding. Hybrid GPU-CPU execution has emerged as a promising solution by offloading KV cache management and parts of attention computation to the CPU. However, a key bottleneck remains:
Fan, Jiakun, Zhang, Yanglin, Li, Xiangchen, Nikolopoulos, Dimitrios S.   +3 more
openaire   +2 more sources

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

A Parallel Multi-Objective Cooperative Coevolutionary Algorithm for Optimising Small-World Properties in VANETs

CLEI Electronic Journal, 2014
Cooperative coevolutionary evolutionary algorithms differ from standard evolutionary algorithms’ architecture in that the population is split into subpopulations, each of them optimising only a sub-vector of the global solution vector. All subpopulations
Grégoire Danoy, Julien Schleich, Pascal Bouvry, Bernabé Dorronsoro   +3 more
doaj   +1 more source

Divided we stand: Parallel distributed stack memory management [PDF]

, 1994
We present an overview of the stack-based memory management techniques that we used in our non-deterministic and-parallel Prolog systems: &-Prolog and DASWAM.
Kish, Shen, Hermenegildo, Manuel V.
core  

Compact Modeling of Volatile‐Switching Electrochemical Metallization Memory Cells by Means of the Electromotive Force

Advanced Intelligent Systems, EarlyView.
A volatile‐switching compact model of electrochemical metallization memory cells for neuromorphic architecture is developed and validated by reliable reproduction of device characterization measurements: I−V sweeps, SET kinetics, relaxation dynamics.
Rana Walied Ahmad, Solomon Amsalu Chekol, Julius Rasbach, Susanne Hoffmann‐Eifert, Stephan Menzel   +4 more
wiley   +1 more source

Study of FPGA-based Error-controllable Floating-point Operation Accelerators [PDF]

Jisuanji gongcheng
Floating-point operations are fundamental operations in the field of High-Performance Computing(HPC). In the context of big data and cloud computing,the amount of data that HPC platforms need to process is continuously growing,and the round-off error of ...
GUAN Mingxiao, LIU Jiakun, ZHANG Hongrui, HE Anping
doaj   +1 more source

Artificial Intelligence for Advanced Functional Materials: Progress and Emerging Frontiers

Advanced Intelligent Systems, EarlyView.
Artificial intelligence is transforming the discovery of functional materials by linking synthesis, characterization, simulation, and design in unified workflows. Advances in machine learning, autonomous experimentation, and foundation models are accelerating innovation across energy, electronics, and biomedicine, while revealing new frontiers for ...
Cristiano Malica, Kostya S. Novoselov, Seongmin Kim, Yousung Jung, Silvana Botti, Miguel A. L. Marques, Tigany Zarrouk, Miguel A. Caro, Sanggyu Chong, Michele Ceriotti, Marivi Fernandez‐Serra, Sara Navarro‐Rodriguez, Aakash Ashok Naik, Janine George, Ömer Hasan Omar, Victor Trinquet, Gian‐Marco Rignanese, Andrey Ustyuzhanin, Sonia Conesa‐Boj, Juan Rojo, Minh‐Tuan Dau, Simon M.‐M. Dubois, Jean‐Christophe Charlier, Jose H. Garcia, Romain Gautier, Rachid Laref, Junfeng Qiao, Giovanni Pizzi, Nicola Marzari, Camilla Coletti, Antonio Rossi, Jan‐Lucas Uslu, Chiara Zanardi, Vincenzo Palermo, Wide Hogenhout, Natalia Konchakova, Peter Klein, Tejs Vegge, Stephan Roche   +38 more
wiley   +1 more source