Quantization-based chained privacy-preserving federated learning by Ya Liu, Shumin Wu, Yibo Li, Fengyu Zhao, Yanli Ren

“…However, traditional FL schemes face significant challenges regarding communication efficiency, computational costs, and privacy preservation. …”
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Lightweight Road Environment Segmentation using Vector Quantization by J. Kwag, A. Yilmaz, C. Toth

“…In this work, we combined vector quantization with the lightweight image segmentation model MobileUNETR and used it as a baseline model for comparison to demonstrate its efficiency. …”
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Efficient human activity recognition on edge devices using DeepConv LSTM architectures by Haotian Zhou, Xiujun Zhang, Yu Feng, Tongda Zhang, Lijuan Xiong

“…The device’s memory usage was 29.1 KB, flash usage was 189.6 KB, and the model’s average inference time was 21 milliseconds, requiring approximately 0.01395 GOP, with a computational performance of around 0.664 GOPS. Even after quantization, the model maintained an accuracy of 97% and an F1 score of 97%, ensuring efficient utilization of computational resources. …”
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A Novel Mixed-Precision Quantization Approach for CNNs by Dan Wu, Yanzhi Wang, Yuqi Fei, Guowang Gao

“…Mixed-precision quantization assigns different quantization precision to different layers of a CNN. …”
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Qptimization design of video encoder quantizer for general DSPs by GAN Yong1, SU Shi-mei2, ZHOU Bing2, QIAN De-pei 1

“…A new scheme of quantization which was constructed by an optimal quantization matrix was proposed.It was inefficient for the traditional quantization of MPEG video encoders on the general DSPs.Considering the traits of a DSP,which could realized one-step quantization by substituting shift for division.Meanwhile,this scheme used different quantization strategies according to the differences of the types of video blocks to be coded,and avoids the loss of the quality of images with the unified quantization.Experiments had shown that this scheme could obviously improve the efficiency of quantization,and subjective and objective quality of images coded as well.…”
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Fully Quantized Neural Networks for Audio Source Separation by Elad Cohen, Hai Victor Habi, Reuven Peretz, Arnon Netzer

“…In this work, we focus on quantization, a leading approach for addressing these challenges. …”
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Quantized convolutional neural networks: a hardware perspective by Li Zhang, Olga Krestinskaya, Mohammed E. Fouda, Ahmed M. Eltawil, Khaled Nabil Salama

“…Consistently, dedicated hardware accelerators are developed to further boost the execution efficiency of DNN models. In this work, we focus on Convolutional Neural Network (CNN) as an example of DNNs and conduct a comprehensive survey on various quantization and quantized training methods. …”
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Peculiarities of Assessing the Efficiency of Research Organization Divisions by E. A. Grigoreva, N. M. Pahnovskay

“…Quantitative analysis is one of simple and frequently used approaches to estimating efficiency of organization and its divisions work. …”
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Edge-Optimized Deep Learning Architectures for Classification of Agricultural Insects with Mobile Deployment by Muhammad Hannan Akhtar, Ibrahim Eksheir, Tamer Shanableh

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Developing Methodological Tools to Estimate Efficiency of Research Organization Divisions by E. A. Grigoreva

Subjects: “…quantitative and qualitative analysis…”
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Ground-truth-free deep learning approach for accelerated quantitative parameter mapping with memory efficient learning by Naoto Fujita, Suguru Yokosawa, Toru Shirai, Yasuhiko Terada

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Remineralizing efficiency of two dentifrices clinically evaluated by DIAGNOdent quantitative light-induced fluorescence, in vivo study by Carla Tatiana Bravo Iñaguazo, Mayra Alejandra Núñez Aldaz, David Flores, Ana Del Carmen Armas Vega

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Construction and application of a quantitative efficiency assessment model for green AI in intelligent customer service systems by MA Xiaoliang, LIU Ying, ZHAO Ruqiang, YANG Bangxing, GAO Jie, DENG Congjian

“…An assessment method for the energy consumption of AI large models was introduced, aimed at quantitatively evaluating the service efficiency (E) of AI models. …”
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Automatic Quantitative Analysis of Internal Quantum Efficiency Measurements of GaAs Solar Cells Using Deep Learning by Zubair Abdullah‐Vetter, Brendan Wright, Tien‐Chun Wu, Ali Shakiba, Ziv Hameiri

“…The proposed method is demonstrated to achieve a very high level of prediction accuracy across the entire range of parameter values and exhibits a high resilience for noisy measurements. By enhancing the quantitative analysis of IQE measurements, the method will unlock the full potential of quantum efficiency measurements as a powerful characterization tool for diverse solar cell technologies.…”
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Hardware-Efficient Neural Network Digital Predistortion for Terahertz Power Amplifiers Using DeepShift and Pruning by Taishi Watanabe, Takeo Ohseki, Issei Kanno

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Bridging the Gap Between Computational Efficiency and Segmentation Fidelity in Object-Based Image Analysis by Fernanda Pereira Leite Aguiar, Irenilza de Alencar Nääs, Marcelo Tsuguio Okano

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THE ANALYSIS OF THE NOISE OF QUANTIZATION OF LINEAR STATIONARY FILTERS OF IMAGE PROCESSING by Nikolay Ivanovich Chervyakov, Pavel Alekseevich Lyakhov, Nikolay Nikolaevich Nagornov

“…The problem of quantization of the coefficients of arbitrary linear stationary filters in order to minimize the noise of this phenomenon and efficient hardware implementation of digital image processing methods is investigated in the paper. …”
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COMQ: A Backpropagation-Free Algorithm for Post-Training Quantization by Aozhong Zhang, Zi Yang, Naigang Wang, Yingyong Qi, Jack Xin, Xin Li, Penghang Yin

“…Post-training quantization (PTQ) has emerged as a practical approach to compress large neural networks, making them highly efficient for deployment. …”
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FPGA-QNN: Quantized Neural Network Hardware Acceleration on FPGAs by Mustafa Tasci, Ayhan Istanbullu, Vedat Tumen, Selahattin Kosunalp

“…The FPGA-QNN framework comes up with 12 accelerators based on multi-layer perceptron (MLP) and LeNet CNN models, each of which is associated with a specific combination of quantization and folding. The outputs from the performance evaluations on Xilinx PYNQ Z1 development board proved the superiority of FPGA-QNN in terms of resource utilization and energy efficiency in comparison to several recent approaches. …”
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Approaching the Bioeconomy in Terms of Increasing the Energy Efficiency of Households in Romania by Gabriel Brătucu, Cristinel Petrișor Constantin, Ioana Bianca Chițu, Evelina Grădinaru, Lavinia Dovleac

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