Effect of coarsely quantization on next generation systems with low-density parity check codes by Ehsan Mostafapour, Changiz Ghobadi, Javad Nourinia

Subjects: Get full text

Performance and Efficiency Comparison of U-Net and Ghost U-Net in Road Crack Segmentation with Floating Point and Quantization Optimization by Haidhi Angkawijana Tedja, Onno W. Purbo

Subjects: Get full text

Efficient secure federated learning aggregation framework based on homomorphic encryption by Shengxing YU, Zhong CHEN

“…In order to solve the problems of data security and communication overhead in federated learning, an efficient and secure federated aggregation framework based on homomorphic encryption was proposed.In the process of federated learning, the privacy and security issues of user data need to be solved urgently.However, the computational cost and communication overhead caused by the encryption scheme would affect the training efficiency.Firstly, in the case of protecting data security and ensuring training efficiency, the Top-K gradient selection method was used to screen model gradients, reducing the number of gradients that need to be uploaded.A candidate quantization protocol suitable for multi-edge terminals and a secure candidate index merging algorithm were proposed to further reduce communication overhead and accelerate homomorphic encryption calculations.Secondly, since model parameters of each layer of neural networks had characteristics of the Gaussian distribution, the selected model gradients were clipped and quantized, and the gradient unsigned quantization protocol was adopted to speed up the homomorphic encryption calculation.Finally, the experimental results show that in the federated learning scenario, the proposed framework can protect data privacy, and has high accuracy and efficient performance.…”
Get full text

Efficient Capacity-Based Joint Quantized Precoding and Transmit Antenna Selection Using Cross-Entropy Method for Multiuser MIMO Systems by Jung-Chieh Chen, Min-Han Chiu, Yi-Syun Yang, Kuan-Yuen Liao, Chih-Peng Li

Get full text

SparseBatch: Communication-efficient Federated Learning with Partially Homomorphic Encryption by Chong Wang, Jing Wang, Zheng Lou, Linghai Kong, WeiSong Tao, Yun Wang

Get full text

Toward resource-efficient UAV systems: Deep learning model compression for onboard-ready weed detection in UAV imagery by Alwaseela Abdalla, Masara M.A. Mohammed, Oluwatola Adedeji, Peter Dotray, Wenxuan Guo

“…We fine-tuned the pruned model on the UAV dataset to mitigate any performance loss resulting from pruning. We then applied quantization to reduce the precision of numerical parameters and improve computational efficiency. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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.…”
Get full text

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. …”
Get full text

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. …”
Get full text

Edge-Optimized Deep Learning Architectures for Classification of Agricultural Insects with Mobile Deployment by Muhammad Hannan Akhtar, Ibrahim Eksheir, Tamer Shanableh

Subjects: Get full text

Hardware-Efficient Neural Network Digital Predistortion for Terahertz Power Amplifiers Using DeepShift and Pruning by Taishi Watanabe, Takeo Ohseki, Issei Kanno

Get full text

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

Subjects: Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

Optimizing binary neural network quantization for fixed pattern noise robustness by Francisco Javier Andreo-Oliver, Gines Domenech-Asensi, Jose Angel Diaz-Madrid, Ramon Ruiz-Merino, Juan Zapata-Perez

“…Abstract This work presents a comprehensive analysis of how extreme data quantization and fixed pattern noise (FPN) from CMOS imagers affect the performance of deep neural networks for image recognition tasks. …”
Get full text