Results 161 to 170 of about 376,317 (337)

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

Advanced 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, Oliver Artner, Paul‐Philipp Manea, Sebastian Siegel, Susanne Hoffmann‐Eifert, Regina Dittmann, John Paul Strachan   +6 more
wiley   +1 more source

Approximately Quantized Thermal Hall Effect of Chiral Liquids Coupled to Phonons [PDF]

, 2018
Yuval Vinkler-Aviv, Achim Rosch
openalex   +1 more source

Input Sparsity‐Aware Computing‐In‐Memory with Bidirectional Conversion‐Skippable Analog‐to‐Digital Converter

Advanced Intelligent Systems, EarlyView.
This article introduces an input sparsity‐aware computing‐in‐memory macro featuring novel bidirectional conversion‐skippable analog‐to‐digital converters. By dynamically adjusting resolution based on element‐level sparsity, the architecture skips redundant most significant bit and least significant bit conversions.
Choongseok Song, Yonguk Sim, Doo Seok Jeong   +2 more
wiley   +1 more source

Quantizing string theory in AdS5×S5: beyond the pp-wave [PDF]

, 2003
Curtis G. Callan, Hok Kong Lee, Tristan McLoughlin, John H. Schwarz, Ian Swanson, Xinkai Wu   +5 more
openalex   +1 more source

Methods for Setting Device Specifications for Analog In‐Memory Computing Inference

Advanced Intelligent Systems, EarlyView.
Non‐volatile memories (NVMs) are being developed for analog in‐memory computing for energy‐efficient, high‐speed deep learning inference. As technology is moving to industry adoption, a method to define required NVM specifications is critical for improving performance and reducing manufacturing cost.
Zhenyu Wu, Xin Su, Malte J. Rasch, Ning Li   +3 more
wiley   +1 more source

Sparse-selective quantization for real-time cyber threat detection in large-scale networks. [PDF]

PLoS One
Xie Y, Wang R, Dong L.
europepmc   +1 more source

Energy quantization for Willmore surfaces and applications [PDF]

, 2014
Yann Bernard, Tristan Rivière
openalex   +1 more source

Quantizing novelty [PDF]

Cytometry Part A, 2011
openaire   +2 more sources

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

Advanced 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

Lightweight Real-Time Navigation for Autonomous Driving Using TinyML and Few-Shot Learning. [PDF]

Sensors (Basel)
Ali W, Iqbal A, Wadood A, Park H, Kang BO.   +4 more
europepmc   +1 more source