Energy efficiency joint optimization algorithm for low-precision ADC massive MIMO systems by Haiyan CAO, Wenjuan HU, Yingjuan ZHI, Fangmin XU, Xin FANG

“…An uplink energy efficiency optimization algorithm based on alternating optimization algorithm was proposed for large-scale multiple-input multiple-output (MIMO) systems with low-precision analog to digital converters (ADC).That is,based on the maximum ratio combining (MRC) reception,an optimization model aiming at maximizing energy efficiency was established,and then the original fractional form optimization problem was converted into an equivalent subtractive form according to the nature of the fractional programming.In the case where the number of quantization bits was constant,the iterative optimization of the transmission power and the pilot length were performed.Simulation results show that the proposed algorithm has better spectral efficiency and energy efficiency.…”
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Edge-assisted UAV onboard video compression and transmission for efficient inference of patrolling tasks by YANG Peng, LIANG Yuxin, KONG Yuxin, LIU Mingliu

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Evaluation of fluxon synapse device based on superconducting loops for energy efficient neuromorphic computing by Ashwani Kumar, Uday S. Goteti, Ertugrul Cubukcu, Robert C. Dynes, Duygu Kuzum

“…Here, we evaluate performance prospects of a new approach based on disordered superconducting loops with Josephson-junctions for energy efficient neuromorphic computing. Synaptic weights can be stored as internal trapped fluxon states of three superconducting loops connected with multiple Josephson-junctions (JJ) and modulated by input signals applied in the form of discrete fluxons (quantized flux) in a controlled manner. …”
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Efficient Limb Range of Motion Analysis from a Monocular Camera for Edge Devices by Xuke Yan, Linxi Zhang, Bo Liu, Guangzhi Qu

“…Our model uses a compact neural network architecture with 8-bit quantized parameters for enhanced memory efficiency and reduced latency. …”
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Learning Online MEMS Calibration with Time-Varying and Memory-Efficient Gaussian Neural Topologies by Danilo Pietro Pau, Simone Tognocchi, Marco Marcon

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AFQSeg: An Adaptive Feature Quantization Network for Instance-Level Surface Crack Segmentation by Shaoliang Fang, Lu Lu, Zhu Lin, Zhanyu Yang, Shaosheng Wang

“…To address these issues, this paper proposes a crack detection model based on adaptive feature quantization, which primarily consists of a maximum soft pooling module, an adaptive crack feature quantization module, and a trainable crack post-processing module. …”
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NeuBridge: bridging quantized activations and spiking neurons for ANN-SNN conversion by Yuchen Yang, Jingcheng Liu, Chengting Yu, Chengyi Yang, Gaoang Wang, Aili Wang

“…Spiking neural networks (SNNs) offer a promising avenue for energy-efficient computations on neuromorphic hardware, leveraging the unique advantages of spike-based signaling. …”
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Decentralized non-convex online optimization with adaptive momentum estimation and quantized communication by Yunshan Lv, Hailing Xiong, Fuqing Zhang, Shengying Dong, Xiangguang Dai

“…To solve the problem over a communication-efficient manner, we propose a novel quantized decentralized adaptive momentum gradient descent algorithm based on the adaptive momentum estimation methods, where quantified information is exchanged between agents. …”
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Randomized Quantization for Privacy in Resource Constrained Machine Learning at-the-Edge and Federated Learning by Ce Feng, Parv Venkitasubramaniam

“…Through rigorous theoretical analysis and extensive experiments on benchmark datasets, we demonstrate that these methods significantly enhance the utility-privacy trade-off and computational efficiency in both ML-at-the-edge and FL systems. RQP-SGD is evaluated on MNIST and the Breast Cancer Diagnostic dataset, showing an average 10.62% utility improvement over the deterministic quantization-based projected DP-SGD while maintaining (1.0, 0)-DP. …”
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Convolution Smooth: A Post-Training Quantization Method for Convolutional Neural Networks by Yongyuan Chen, Zhendao Wang

“…Convolutional neural network (CNN) quantization is an efficient model compression technique primarily used for accelerating inference and optimizing resources. …”
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Lost-minimum post-training parameter quantization method for convolutional neural network by Fan ZHANG, Yun HUANG, Zizhuo FANG, Wei GUO

“…To solve the problem that that no dataset is available for model quantization in data-sensitive scenarios, a model quantization method without using data sets was proposed.Firstly, according to the parameters of batch normalized layer and the distribution characteristics of image data, the simulated input data was obtained by error minimization method.Then, by studying the characteristics of data rounding, a factor dynamic rounding method based on loss minimization was proposed.Through quantitative experiments on classification models such as GhostNet and target detection models such as M2Det, the effectiveness of the proposed quantification method for image classification and target detection models was verified.The experimental results show that the proposed quantization method can reduce the model size by about 75%, effectively reduce the power loss and improve the computing efficiency while basically maintaining the accuracy of the original model.…”
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Hierarchical Mixed-Precision Post-Training Quantization for SAR Ship Detection Networks by Hang Wei, Zulin Wang, Yuanhan Ni

“…However, limited satellite platform resources present a significant challenge. Post-training quantization (PTQ) provides an efficient method for pre-training neural networks to effectively reduce memory and computational resources without retraining. …”
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A Novel Method to Improve the Efficiency and Performance of Cloud-Based Visual Simultaneous Localization and Mapping by Omar M. Salih, Hussam Rostum, József Vásárhelyi

“…This paper proposes a novel solution to improve the efficiency and performance of exchanging data between the unmanned aerial vehicle (UAV) and the cloud server. …”
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CSCP-YOLO: A Lightweight and Efficient Algorithm for Real-Time Steel Surface Defect Detection by Chenglong Wang, Heng Wang, Yimin Jiang, Lei Yu, Xueting Wang

“…To accelerate defect detection, the SPPF-DW module efficiently fuses multi-scale defect features through optimized computational pathways. …”
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Fully Quantized Matrix Arithmetic-Only BERT Model and Its FPGA-Based Accelerator by Hiroshi Fuketa, Toshihiro Katashita, Yohei Hori, Masakazu Hioki

“…In this paper, we propose a fully quantized matrix arithmetic-only BERT (FQ MA-BERT) model to enable efficient natural language processing. …”
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Research of channel quantization and feedback strategies based on multiuser diversity MIMO-OFDM systems by LIANG Xue-jun, ZHU Guang-xi, SU Gang, WANG De-sheng

“…Firstly, a quantization method was proposed by quantized value indicating the modulation level instead of the full values of channel quality information（CQI） and the achievable average spectrum efficiency showed no loss compared with perfect case.Secondly, employment of the integrated design that combined with opportunistic, best, and hybrid feedback scheme was considered and the close-form expression of average spectrum efficiency was deduced in various case.Finally, the calculation of optimal feedback parameters was confirmed from two aspects of feedback channel capacity and capacity relative loss.Extensive simulations were presented to evaluate these proposed strategies.The results match with the numeral analysis very well.The proposed partial feedback schemes can reduce the feedback load greatly with the same system capability, only if the feedback parameters be chosen properly.Wherein, the hybrid feedback combined with quantization performs best and provides the instruction to design the channel feedback of practical systems.…”
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Optimizing Deep Learning Models for Resource‐Constrained Environments With Cluster‐Quantized Knowledge Distillation by Niaz Ashraf Khan, A. M. Saadman Rafat

“…These results highlight the feasibility of CQKD for efficient deep learning model deployment in low‐resource environments.…”
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Study of algorithmic approaches to digital signal filtering and the influence of input quantization on output accuracy by Shermuradova Malika, Gadoeva Mavlyuda, Rahmatov Shukhrat, Abdullaev Uktamjon, Aralov Dilshod

“…The research supports the broader integration of AI-driven technologies in modern automation systems, paving the way for more adaptive, efficient, and fault-tolerant control mechanisms in complex environments.…”
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Smoothed per-tensor weight quantization: a robust solution for neural network deployment by Xin Chang

“…This paper introduces a novel method to improve quantization outcomes for per-tensor weight quantization, focusing on enhancing computational efficiency and compatibility with resource-constrained hardware. …”
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Two-dimensional materials based two-transistor-two-resistor synaptic kernel for efficient neuromorphic computing by Qian He, Hailiang Wang, Yishu Zhang, Anzhe Chen, Yu Fu, Guodong Xue, Kaihui Liu, Shiman Huang, Yang Xu, Bin Yu

“…Here, we develop a 16 × 16 computing kernel based on two-transistor-two-resistor unit with three-dimensional heterogeneous integration compatibility to boost energy efficiency and computing performance. We demonstrate the 4-bit weight characteristics of artificial synapses with low stochasticity. …”
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