Efficient Real-Time Isotope Identification on SoC FPGA by Katherine Guerrero-Morejón, José María Hinojo-Montero, Jorge Jiménez-Sánchez, Cristian Rocha-Jácome, Ramón González-Carvajal, Fernando Muñoz-Chavero

“…Efficient real-time isotope identification is a critical challenge in nuclear spectroscopy, with important applications such as radiation monitoring, nuclear waste management, and medical imaging. …”
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SpiNeRF: direct-trained spiking neural networks for efficient neural radiance field rendering by Xingting Yao, Xingting Yao, Qinghao Hu, Fei Zhou, Tielong Liu, Tielong Liu, Zitao Mo, Zeyu Zhu, Zeyu Zhu, Zhengyang Zhuge, Jian Cheng, Jian Cheng

“…Experiments on multiple datasets demonstrate that our method outperforms previous SNN encoding schemes and artificial neural network (ANN) quantization methods in both rendering quality and energy efficiency. …”
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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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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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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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Fully Quantized Neural Networks for Audio Source Separation by Elad Cohen, Hai Victor Habi, Reuven Peretz, Arnon Netzer

“…We validate the efficiency of our method in both time and frequency domains. …”
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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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An efficient post-processing adaptive filtering technique to rectifying the flickering effects. by Anudeep Gandam, Jagroop Singh Sidhu, Sahil Verma, N Z Jhanjhi, Anand Nayyar, Mohamed Abouhawwash, Yunyoung Nam

“…This paper focuses on an efficient pre-and post-processing technique (PP-AFT) to address and rectify the problems of quantization error, ringing, blocking artifact, and flickering effect, which significantly degrade the visual quality of video frames. …”
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Feature-Based Federated Transfer Learning: Communication Efficiency, Robustness and Privacy by Feng Wang, M. Cenk Gursoy, Senem Velipasalar

“…In this paper, we propose feature-based federated transfer learning as a novel approach to improve communication efficiency by reducing the uplink payload by multiple orders of magnitude compared to that of existing approaches in federated learning and federated transfer learning. …”
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An adaptive dual distillation framework for efficient remaining useful life prediction by Xiang Cheng, Jun Kit Chaw, Shafrida Sahrani, Mei Choo Ang, Saraswathy Shamini Gunasekaran, Moamin A. Mahmoud, Halimah Badioze Zaman, Yanfeng Zhao, Fuchen Ren

“…These results highlight A-DDF’s potential for efficient, high-accuracy predictive maintenance on edge devices. …”
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Parameter-Efficient Adaptation of Large Vision—Language Models for Video Memorability Prediction by Iván Martín-Fernández, Sergio Esteban-Romero, Fernando Fernández-Martínez, Manuel Gil-Martín

“…The accurate modelling of video memorability, or the intrinsic properties that render a piece of audiovisual content more likely to be remembered, will facilitate the development of automatic systems that are more efficient in retrieving, classifying and generating impactful media. …”
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Design and Implementation of a Lightweight and Energy-Efficient Semantic Segmentation Accelerator for Embedded Platforms by Hui Li, Jinyi Li, Bowen Li, Zhengqian Miao, Shengli Lu

“…With the rapid development of lightweight network models and efficient hardware deployment techniques, the demand for real-time semantic segmentation in areas such as autonomous driving and medical image processing has increased significantly. …”
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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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Spike-RISC: Algorithm/ISA Co-Optimization for Efficient SNNs on RISC-V by Ipek Akdeniz, Sandy A. Wasif, Paul R. Genssler, Hussam Amrouch

“…This paper proposes Spike-RISC, a holistic algorithm/instruction set architecture (ISA) co-optimization for efficient SNN inference. Our key algorithm optimizations include quantizing the weights to just eight bits and exploiting the network’s sparsity in hardware and software. …”
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High-Fidelity and High-Efficiency Digital Class-D Audio Power Amplifier by Cong Wei, Jianhan Wu, Rongshan Wei, Minghua He

“…This study presents a high-fidelity and high-efficiency digital class-D audio power amplifier (CDA), which consists of digital and analog modules. …”
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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. …”
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Quantized Feedback Control of Active Suspension Systems Based on Event Trigger by Jinwei Sun, Jingyu Cong, Weihua Zhao, Yonghui Zhang

“…In addition, the trigger mechanism can improve the working efficiency of actuators effectively.…”
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