Results 201 to 210 of about 14,778,232 (317)

Device Stochasticity Directly Implements Bayesian Sampling in Spiking Neurons

open access: yesAdvanced Science, EarlyView.
Brain‐inspired stochastic spiking turns device noise into Bayesian uncertainty for robust, low‐power neuromorphic computing. ABSTRACT Device stochasticity is considered a non‐ideality in neuromorphic hardware, although stochastic neural activity supports probabilistic computation in biological systems.
Huannan Zheng, Jingli Liu, Kezhou Yang
wiley   +1 more source

Conformal Reconfigurable Intelligent Surfaces: A Cylindrical Geometry Perspective

open access: yesAdvanced Electronic Materials, EarlyView.
Cylindrical reconfigurable intelligent surfaces are explored for low‐complexity beam steering using one‐bit meta‐atoms. A multi‐level modeling approach, including optimization‐based synthesis, demonstrates that even minimal hardware can support directive scattering.
Filippo Pepe   +4 more
wiley   +1 more source

Robust and Compatible Ferroelectric Memories with Polycrystalline TiO2 Channel for 3D Integration

open access: yesAdvanced Electronic Materials, EarlyView.
Robust and monolithic 3D compatible ferroelectric memories are realized using the polycrystalline TiO2 channel‐based FeFET. The review covers physical mechanisms of the TiO2 channel FeFET, quantitative benchmarking, and advanced planar/vertical architectures for monolithic 3D integration based on HfO2‐TiO2 gate stack, offering a roadmap for reliable ...
Xujin Song   +10 more
wiley   +1 more source

Predictions of rhizosphere microbiome dynamics with a genome-informed and trait-based energy budget model. [PDF]

open access: yesNat Microbiol
Marschmann GL   +7 more
europepmc   +1 more source

Earth's Global Energy Budget

open access: yes, 2009
K. Trenberth, J. Fasullo, J. Kiehl
semanticscholar   +1 more source

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

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

Stochastic‐MTJ Sampler Arrays for In‐Array Monte–Carlo Estimation

open access: yesAdvanced Electronic Materials, EarlyView.
A low‐energy‐barrier magnetic tunnel junction array is operated as a probability‐domain sampler: each cell's random switching, programmed through a shared digital‐to‐analog converter, makes the per‐column multiply–accumulate an unbiased Monte–Carlo expectation estimator that returns both a mean and a calibrated uncertainty.
Ran Zhang   +6 more
wiley   +1 more source

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