Enabling Flexible Link Capacity for eCPRI-Based Fronthaul With Load-Adaptive Quantization Resolution by Longsheng Li, Meihua Bi, Haiyun Xin, Yunhao Zhang, Yan Fu, Xin Miao, Ahmed Mohanmmed Mikaeil, Weisheng Hu

“…Bandwidth-efficient 5G optical fronthaul interfaces, such as the Ethernet-based common public radio interface (eCPRI), with novel low layer split (LLS) are being actively investigated. …”
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FPGA Acceleration With Hessian-Based Comprehensive Intra-Layer Mixed-Precision Quantization for Transformer Models by Woohong Byun, Jongseok Woo, Saibal Mukhopadhyay

“…Our concurrent quantization method balances the benefits of row-wise weight quantization and Query-Key coupled activation quantization while maximizing energy efficiency through multi-precision optimization. …”
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GENERATING OF OPTIMAL QUANTIZATION LEVELS OF CONTROL CURRENTS FOR LINEAR STEPPING DRIVES OF PRECISION MOTION SYSTEMS by I. V. Dainiak, V. P. Oger, D. S. Tsitko, D. G. Begun

“…The investigations have proved an efficiency of the proposed algorithm and methodology for forming coordinate discrete grid.…”
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Sliding Mode Control of Uncertain Switched Systems via Length-Limited Coding Dynamic Quantization by Qinqi Xu, Haijuan Zhao

“…To address these issues, a dynamic quantizer is introduced to efficiently encode the system state while minimizing quantization error under the constraint of the finite code length. …”
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QDLTrans: Enhancing English Neural Machine Translation With Quantized Attention Block and Tunable Dual Learning by Xing Liu

“…Specifically, our method outperforms existing approaches in both translation quality and efficiency, offering a balanced solution for enhancing NMT in low-resource settings.…”
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An Efficient Ship Target Integrated Imaging and Detection Framework (ST-IIDF) for Space-Borne SAR Echo Data by Can Su, Wei Yang, Yongchen Pan, Hongcheng Zeng, Yamin Wang, Jie Chen, Zhixiang Huang, Wei Xiong, Jie Chen, Chunsheng Li

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CALCULATION OF ELASTICALLY STRESSED QUANTUM WELLS HETEROSTRUCTURE AlXGaYIn1-X-YAs/InP FOR EFFICIENT DIODE LASERS by V. N. Svetogorov, R. Kh. Akchurin, A. A. Marmalyuk, M. A. Ladugin, I. V. Yarotskaya

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Deep combining of local phase quantization and histogram of oriented gradients for indoor positioning based on smartphone camera by Jichao Jiao, Zhongliang Deng

“…To address these issues, we propose a new feature descriptor by deeply combining histogram of oriented gradients and local phase quantization. This feature is a local phase quantization of a salient histogram of oriented gradient visualizing image, which is robust in indoor scenarios. …”
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NeuroDetect: Deep Learning-Based Signal Detection in Phase-Modulated Systems with Low-Resolution Quantization by Chanula Luckshan, Samiru Gayan, Hazer Inaltekin, Ruhui Zhang, David Akman

“…We rigorously investigate the interplay between ADC resolution and detection accuracy, introducing novel penalty metrics that quantify the effects of both quantization and learning errors. Our results shed light on the design trade-offs between ADC resolution and detection accuracy, providing future directions for developing energy-efficient high-speed and wideband wireless systems.…”
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A Dual-Mode Compatible CT ADC With FIR DAC and SB Quantization for DSM and IDSM Operations by Han Yang, Yuqi Wang, Ying Hou, Xiaosong Wang, Yu Liu

“…At the circuit level, a compact and energy-efficient compensation FIR DAC is implemented based on a configurable capacitive passive summation network. …”
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TCL: Time-Dependent Clustering Loss for Optimizing Post-Training Feature Map Quantization for Partitioned DNNs by Oscar Artur Bernd Berg, Eiraj Saqib, Axel Jantsch, Irida Shallari, Silvia Krug, Isaac Sanchez Leal, Mattias O'Nils

“…TCL regularization clusters activations at the partitioning point to align with quantization levels, minimizing quantization error and ensuring accuracy even with extreme low-bitwidth quantization. …”
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Fuzzy Course Tracking Control of Unmanned Surface Vehicle with Actuator Input Quantization and Event-Triggered Mechanism by Qifu Wang, Chenchen Jiang, Jun Ning, Liying Hao, Yong Yin

“…The control system’s control inputs are quantized by using uniform quantizers. Then, the event-triggered adaptive fuzzy quantization control method is introduced, which can reduce the frequency of control actions and effectively reduce the communication burden. …”
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Quantized Auto Encoder-Based Anomaly Detection for Multivariate Time Series Data in 5G Networks by Giovanni Trappolini, Antonio Purificato, Federico Siciliano, Luigi D'Addona, Anna Maria Spagnolo, Domenico Dato, Fabrizio Silvestri

“…This paper introduces <monospace>QAED</monospace> (Quantized Auto Encoder Detector), a novel deep learning approach for anomaly detection in 5G networks with three key innovations: 1) a vector quantization mechanism that effectively captures discrete network states, 2) a kernel density estimation preprocessing step that enables detection of both outliers and distribution shifts, and 3) an integrated architecture that processes multivariate time series data in a unified framework. …”
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Enhancing Autism Spectrum Disorder Classification with Lightweight Quantized CNNs and Federated Learning on ABIDE-1 Dataset by Simran Gupta, Md. Rahad Islam Bhuiyan, Sadia Sultana Chowa, Sidratul Montaha, Rashik Rahman, Sk. Tanzir Mehedi, Ziaur Rahman

“…The proposed 1D-CNN is quantized through Quantize Aware Training (QAT). As the quantization method, int8 quantization is utilized, which makes it both robust and lightweight. …”
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Neural network compression for reinforcement learning tasks by Dmitry A. Ivanov, Denis A. Larionov, Oleg V. Maslennikov, Vladimir V. Voevodin

“…Abstract In real applications of Reinforcement Learning (RL), such as robotics, low latency, energy-efficient and high-throughput inference is very desired. …”
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InMemQK: A Product Quantization Based MatMul Module for Compute-in-Memory Attention Macro by Pengcheng Feng, Yihao Chen, Jinke Yu, Hao Yue, Zhelong Jiang, Yi Xiao, Wan’ang Xiao, Huaxiang Lu, Gang Chen

“…At the software level, InMemQK employs product quantization (PQ) to eliminate data dependencies. At the hardware level, InMemQK integrates energy-efficient time-domain MAC macros for ADC-free computations. …”
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Digital Spectral Analysis by means of the Method of Averag Modified Periodograms Using Binary-Sign Stochastic Quantization of Signals by V. N. Yakimov

“…The aim of this work was the development of mathematical and algorithmic support, which can increase the computational efficiency of signals digital spectral analysis by this method.The solution to this problem is based on the use of binary-sign stochastic quantization for converting the analyzed signal into a digital code. …”
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Quantization-Based Jailbreaking Vulnerability Analysis: A Study on Performance and Safety of the Llama3-8B-Instruct Model by Jaesik Lee

“…This study systematically investigates how quantization, a key technique for the efficient deployment of large language models (LLMs), affects model safety. …”
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A dynamic simulation method of urban rail traction power supply system based on quantized state time discretization by SHI Xiangwen, LIN Fei, WANG Chao, WU Xiaobo, SHA Donglei, YANG Zhongping

“…The results show that the quantized state time discretization hybrid solution method is feasible, accurate and efficient in the dynamic simulation system of urban rail traction power supply.…”
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A KWS System for Edge-Computing Applications with Analog-Based Feature Extraction and Learned Step Size Quantized Classifier by Yukai Shen, Binyi Wu, Dietmar Straeussnigg, Eric Gutierrez

“…The filter bank is behaviorally modeled, making use of second-order band-pass transfer functions, simulating the analog front-end (AFE) processing. To enable efficient deployment, the GRU classifier is trained using a Learned Step Size (LSQ) and Look-Up Table (LUT)-aware quantization method. …”
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