Results 141 to 150 of about 148,680 (322)

Emerging Memory and Device Technologies for Hardware‐Accelerated Model Training and Inference

open access: yesAdvanced 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   +6 more
wiley   +1 more source

The Quasistationary Phase Field Equations with Neumann Boundary Conditions

open access: yesJournal of Differential Equations, 2000
The paper considers the quasistationary phase field equations \[ \partial_t(u+ \varphi)- \Delta u= f\quad\text{in }\Omega\times ]0,T[, \] \[ \partial_\nu u= 0\quad\text{on }\partial\Omega\times ]0, T[, \] \[ (u+\varphi)(0)= w_0, \] and \[ -2\varepsilon\Delta\varphi+ {1\over\varepsilon} W'(\varphi)= u\quad\text{in }\Omega\times ]0, T[, \] \[ \partial_ ...
openaire   +1 more source

Influence of Metal Species and Content of Fe‐Ni‐Poly(heptazine imides) on their Properties as Electrocatalysts for Zinc‐Air Batteries

open access: yesAdvanced Energy Materials, EarlyView.
Iron/nickel‐modified poly(heptazine imides) are efficient, low‐cost bifunctional electrocatalysts for the oxygen reduction and evolution reactions in aqueous zinc–air batteries. Iron enhances oxygen reduction, while nickel single‐atoms deliver oxygen evolution activity comparable to RuO2.
Franz Jacobi   +10 more
wiley   +1 more source

Triboelectric Tactile Transducers for Neuromorphic Sensing and Synaptic Emulation: Materials, Architectures, and Interfaces

open access: yesAdvanced Energy and Sustainability Research, EarlyView.
Triboelectric nanogenerators are vital for sustainable energy in future technologies such as wearables, implants, AI, ML, sensors and medical systems. This review highlights improved TENG neuromorphic devices with higher energy output, better stability, reduced power demands, scalable designs and lower costs.
Ruthran Rameshkumar   +2 more
wiley   +1 more source

Designing Memristive Materials for Artificial Dynamic Intelligence

open access: yesAdvanced Intelligent Discovery, EarlyView.
Key characteristics required of memristors for realizing next‐generation computing, along with modeling approaches employed to analyze their underlying mechanisms. These modeling techniques span from the atomic scale to the array scale and cover temporal scales ranging from picoseconds to microseconds. Hardware architectures inspired by neural networks
Youngmin Kim, Ho Won Jang
wiley   +1 more source

Toward Capacitive In‐Memory‐Computing: A Device to Systems Level Perspective on the Future of Artificial Intelligence Hardware

open access: yesAdvanced Intelligent Discovery, EarlyView.
Capacitive, charge‐domain compute‐in‐memory (CIM) stores weights as capacitance,eliminating DC sneak paths and IR‐drop, yielding near‐zero standbypower. In this perspective, we present a device to systems level performance analysis of most promising architectures and predict apathway for upscaling capacitive CIM for sustainable edge computing ...
Kapil Bhardwaj   +2 more
wiley   +1 more source

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