Results 81 to 90 of about 1,211 (280)

Fundamental Challenges, Physical Implementations, and Integration Strategies for Ising Machines in Large‐Scale Optimization Tasks

open access: yesAdvanced Electronic Materials, EarlyView.
Ising machines are emerging as specialized hardware solvers for computationally hard optimization problems. This review examines five major platforms—digital CMOS, analog CMOS, emerging devices, coherent optics, and quantum systems—highlighting physics‐rooted advantages and shared bottlenecks in scalability and connectivity.
Hyunjun Lee, Joon Pyo Kim, Sanghyeon Kim
wiley   +1 more source

A communication platform for distributed PC/mainframe applications within a 3270 environment [PDF]

open access: yes, 2009
Remote personal computer communication with IBM mainframes is often confined to low throughput (less than 19,200 baud), asynchronous serial lines managed by the mainframe through 3270 protocol converters.
Bears, Stephen Gibbs
core   +1 more source

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

open access: yesAdvanced 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   +4 more
wiley   +1 more source

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

open access: yesAdvanced 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   +10 more
wiley   +1 more source

A Subnanosecond LSI Family for Mainframe Technology [PDF]

open access: yes, 1979
A subnanosecond LSI family is defined for next generation mainframes. It employs distributed on-chip regulation to reduce system power supply cost, stacked structures for delay-power improvement, on-chip test/diagnostic monitors and signature circuits to
Muller, H. H., Stopper, H., Tam, R. K.
core  

Stretchable Energy Storage with Eutectic Gallium Indium Alloy

open access: yesAdvanced Energy Materials, Volume 15, Issue 11, March 18, 2025.
A highly stretchable liquid metal‐based electrode is developed via a one‐step process, retaining conductivity and capacitance after mechanical deformation up to 900% strain. The stretchable all‐solid‐state device provides a areal energy density of 43 µWh cm⁻2 after 150% strain.
Adit Gupta   +6 more
wiley   +1 more source

An ORIGEN2 update for PCs and mainframes [PDF]

open access: yes, 1991
The ORIGEN2 computer code was developed by Oak Ridge National Laboratory (ORNL) in the late 1970s and made available to users worldwide in 1980 through the Radiation Shielding Information Center (RSIC). The purpose of ORIGEN2 is to calculate the buildup,
Ludwig, S. B.
core  

Assessing Mesoscale Heterogeneities in Hard Carbon Electrodes Through Deep Learning‐Assisted FIB‐SEM Characterization, Manufacturing and Electrochemical Modeling

open access: yesAdvanced Energy Materials, EarlyView.
A combination of discrete and finite element method models for the current collector deformation and electrochemical performance analysis, respectively. The models are calibrated and validated with electrochemical and imaging data of hard carbon electrodes. These electrodes were manufactured with different parameters (slurry solid contents of 35 and 40
Soorya Saravanan   +12 more
wiley   +1 more source

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

open access: yesAdvanced 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   +19 more
wiley   +1 more source

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