Results 221 to 230 of about 857,070 (293)

A Flexible and Energy‐Efficient Compute‐in‐Memory Accelerator for Kolmogorov–Arnold Networks

open access: yesAdvanced Intelligent Systems, EarlyView.
This article presents KA‐CIM, a compute‐in‐memory accelerator for Kolmogorov–Arnold Networks (KANs). It enables flexible and efficient computation of arbitrary nonlinear functions through cross‐layer co‐optimization from algorithm to device. KA‐CIM surpasses CPU, ASIC, VMM‐CIM, and prior KAN accelerators by 1–3 orders of magnitude in energy‐delay ...
Chirag Sudarshan   +6 more
wiley   +1 more source

Explainable AI‐Driven Optimization of Electrode Activation Reduces Power Consumption While Preserving Object Recognition in Retinal Prostheses

open access: yesAdvanced Intelligent Systems, EarlyView.
Explainable artificial intelligence (XAI) guides selective electrode activation in retinal prostheses by emphasizing visually informative regions. XAI‐assisted phosphene generation maintains object recognition performance while significantly reducing stimulation power.
Sein Kim, Hamin Shim, Maesoon Im
wiley   +1 more source

Artificial Intelligence in Autonomous Mobile Robot Navigation: From Classical Approaches to Intelligent Adaptation

open access: yesAdvanced Intelligent Systems, EarlyView.
Artificial intelligence (AI) is reshaping autonomous mobile robot navigation beyond classical pipelines. This review analyzes how AI techniques are integrated into core navigation tasks, including path planning and control, localization and mapping, perception, and context‐aware decision‐making. Learning‐based, probabilistic, and soft‐computing methods
Giovanna Guaragnella   +5 more
wiley   +1 more source

Tailoring combinational therapy with Monte Carlo method-based regression modeling. [PDF]

open access: yesFundam Res
Wang B   +7 more
europepmc   +1 more source

Activation‐Integrated and Memory‐Assisted Dynamic‐Latch Quantizer for Variation‐Tolerant and Low‐Energy Neuromorphic Computing

open access: yesAdvanced Intelligent Systems, EarlyView.
A memory‐assisted dynamic‐latch ADC integrating charge‐trap flash enables ultra‐low‐energy quantization and in‐ADC nonlinear activation for variation‐tolerant neuromorphic computing. Analog‐to‐digital converters (ADCs) remain the dominant area/energy bottleneck in neuromorphic computing (NC) systems.
Jonghyun Ko   +4 more
wiley   +1 more source

Disentangling Aleatoric and Epistemic Uncertainty in Physics‐Informed Neural Networks: Application to Insulation Material Degradation Prognostics

open access: yesAdvanced Intelligent Systems, EarlyView.
Physics‐Informed Neural Networks (PINNs) provide a framework for integrating physical laws with data. However, their application to Prognostics and Health Management (PHM) remains constrained by the limited uncertainty quantification (UQ) capabilities.
Ibai Ramirez   +4 more
wiley   +1 more source

SiOx‐Based Probabilistic Bits Enabling Invertible Logic Gate for Cryptographic Applications

open access: yesAdvanced Intelligent Systems, EarlyView.
To enable lightweight hardware encryption and decryption, a Ti/SiOx/Ti threshold switching device is engineered to generate controllable stochastic oscillations. By tuning the input voltage, the device produces a programmable spike probability governed by intrinsic switching dynamics, enabling probabilistic bits that construct an invertible ...
Jihyun Kim, Hyeonsik Choi, Jiyong Woo
wiley   +1 more source

A Neuromorphic Simulation Framework for Indium‐Gallium‐Zinc‐Oxide Charge‐Trap Synaptic Transistors: From Device Modeling to System Simulation

open access: yesAdvanced Intelligent Systems, EarlyView.
This work presents a comprehensive framework bridging device fabrication, modeling, and system‐level simulation for an indium‐gallium‐zinc‐oxide (IGZO) charge‐trap synaptic transistor‐based neuromorphic system. By developing a precise SPICE model derived from fabricated IGZO synaptic transistors, the study incorporates parasitic RC loads into array ...
Yumin Yun   +5 more
wiley   +1 more source

Shapley Additive Explanation for Local Class Differentiation: Local Explainability for Class Differentiation in Classification Models

open access: yesAdvanced Intelligent Systems, EarlyView.
An instance‐level, model‐agnostic explanation of class differentiation is introduced through SHAP‐LCD, linking probability shifts to feature‐wise Shapley contributions. The method operates on tabular and image data and is released in a fully reproducible implementation, offering a transparent way to examine, at each instance, why predictive models ...
Roxana M. Romero Luna   +2 more
wiley   +1 more source

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