Results 51 to 60 of about 379,828 (307)
Engineering a Spin‐Orbit Bandgap in Graphene‐Tellurium Heterostructures
Advanced Functional Materials, EarlyView.Tellurium intercalation in epitaxial graphene on Ir(111) enables the emergence of a spin–orbit‐induced bandgap with energy spin splitting. By combining STM, ARPES, spin‐resolved ARPES, and DFT, two structural phases are identified, both exhibiting tunable electronic doping.
Beatriz Muñiz Cano +14 more
wiley +1 more source
ALigN: A Highly Accurate Adaptive Layerwise Log_2_Lead Quantization of Pre-Trained Neural Networks
IEEE Access, 2020 Deep Neural Networks are one of the machine learning techniques which are increasingly used in a variety of applications. However, the significantly high memory and computation demands of deep neural networks often limit their deployment on embedded ...
Siddharth Gupta +4 more
doaj +1 more source
NoisyQuant: Noisy Bias-Enhanced Post-Training Activation Quantization for Vision Transformers [PDF]
arXiv, 2022 The complicated architecture and high training cost of vision transformers urge the exploration of post-training quantization. However, the heavy-tailed distribution of vision transformer activations hinders the effectiveness of previous post-training quantization methods, even with advanced quantizer designs.
arxiv
Advanced Functional Materials, EarlyView.A spin group (SG)‐based mechanism is proposed to realize a single pair of Weyl points. PT‐symmetric nodal lines (NLs) persist under T‐breaking, protected by the combination of SG and P symmetry. When considering spin‐orbit coupling, the SG‐protected NL will split into Weyl points, which will also induce anomalous transport phenomena arising from ...
Shifeng Qian +6 more
wiley +1 more source
Omnidirectional Transmissive Acoustic Metasurfaces Based on Goldberg Polyhedra
Advanced Functional Materials, EarlyView.This study introduces omnidirectional acoustic metasurfaces capable of manipulating wavefronts in multiple arbitrary directions simultaneously. A full‐stack pipeline for design, optimization, and fabrication is presented to construct near‐spherical holograms based on Goldberg polyhedra.
Andrea Achilleos +3 more
wiley +1 more source
Alternative approach to electromagnetic field quantization in nonlinear and inhomogeneous media
, 1998 A simple approach is proposed for the quantization of the electromagnetic field in nonlinear and inhomogeneous media. Given the dielectric function and nonlinear susceptibilities, the Hamiltonian of the electromagnetic field is determined completely by ...
A. N. Kireev +29 more
core +1 more source
Advanced Functional Materials, EarlyView.An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone +11 more
wiley +1 more source
Solving the Insoluble: A New Rule for Quantization
, 2018 The rules of canonical quantization normally offer good results, but sometimes they fail, e.g., leading to quantum triviality ($=$ free) for certain examples that are classically nontrivial ($\ne$ free).
David Pyle (489627) +4 more
core +2 more sources
Quantized passive filtering for switched delayed neural networks
Nonlinear Analysis, 2021 The issue of quantized passive filtering for switched delayed neural networks with noise interference is studied in this paper. Both arbitrary and semi-Markov switching rules are taken into account.
Youmei Zhou +3 more
doaj +1 more source
Genie: Show Me the Data for Quantization [PDF]
arXiv, 2022 Zero-shot quantization is a promising approach for developing lightweight deep neural networks when data is inaccessible owing to various reasons, including cost and issues related to privacy. By exploiting the learned parameters ($\mu$ and $\sigma$) of batch normalization layers in an FP32-pre-trained model, zero-shot quantization schemes focus on ...
arxiv