Binary-Weighted Neural Networks Using FeRAM Array for Low-Power AI Computing by Seung-Myeong Cho, Jaesung Lee, Hyejin Jo, Dai Yun, Jihwan Moon, Kyeong-Sik Min

“…Furthermore, the combination of binary weight quantization and in-memory computing enables energy-efficient inference without significant loss in recognition accuracy, as validated using MNIST datasets. …”
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A Deep Learning-Driven Solution to Limited-Feedback MIMO Relaying Systems by Kwadwo Boateng Ofori-Amanfo, Bridget Durowaa Antwi-Boasiako, Prince Anokye, Suho Shin, Kyoung-Jae Lee

“…By harnessing binary feedback information from the implemented DNNs together with efficient beamforming vectors, a novel approach to the resulting problem is presented. …”
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Microcontroller-Based EdgeML: Health Monitoring for Stress and Sleep via HRV by Priyanshu Srivastava, Namita Shah, Kavita Jaiswal

“…However, traditional ML systems often face challenges in real-time processing and resource efficiency, limiting their application in life-critical scenarios. …”
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Fast jet tagging with MLP-Mixers on FPGAs by Chang Sun, Jennifer Ngadiuba, Maurizio Pierini, Maria Spiropulu

“…By using advanced optimization techniques such as High-Granularity Quantization and Distributed Arithmetic, we achieve unprecedented efficiency. …”
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Copy-Move Forgery Verification in Images Using Local Feature Extractors and Optimized Classifiers by S. B. G. Tilak Babu, Ch Srinivasa Rao

“…The proposed algorithms are more efficient than state-of-the-art algorithms even though the suspected image is post-processed.…”
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Cloud–Edge Collaborative Learning and Model Compression Framework for Communication-Assisted Human Action Recognition by Bo Gao, Quan Zhou

“…The growing demand for efficient human action recognition has led to significant advancements in applications such as surveillance, healthcare, and human–computer interaction. …”
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TANS: A Tolerance-Aware Neighborhood Search Method for Workflow Scheduling with Uncertainties in Cloud Manufacturing by Haiyan Xu, Fanhao Ma, Long Chen

“…Workflow tasks in cloud manufacturing often involve uncertain execution and logistics times due to large-scale and geographically distributed resources, creating significant challenges for efficient and reliable scheduling. To address these challenges, we propose the Tolerance-aware Neighborhood Search (TANS) algorithm, which integrates fuzzy time quantization with heuristic neighborhood search techniques. …”
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DPU empowered intelligent congestion control mechanism for the intelligent computing center network by CHEN Jinqian, GUO Shaoyong, LIU Chang, QI Feng, QIU Xuesong

“…By integrating pruning and quantization, the model was lightweighted. Moreover, the model was transformed into the efficient gradient-boosted decision tree through knowledge distillation method, allowing for precise matching of control actions with real-time network conditions. …”
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Federated Learning for Brain Tumor Diagnosis: Methods, Challenges and Future Prospects by Ma Yuhan

“…Techniques like knowledge distillation, model quantization, and pruning are proposed to enhance computational efficiency and minimize communication costs. …”
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A multi-chroma format cascaded coding method for full-chroma image in AVS2 by Liping ZHAO, Tao LIN, Kailun ZHOU, Keli HU, Chunmei LIN

“…In the second generation of audio video coding standard (AVS2),a multi-chroma format cascaded coding method (MCFCC) for full-chroma (4:4:4 sampling format) images coding was proposed.The MCFCC algorithm firstly converts the 4:4:4 sampling format image into 4:2:0 sampling format image,then the 4:2:0 sampling format image was processed by 4:2:0 sampling format intra prediction,transform,quantization,inverse quantization,inverse transform,entropy coding and a weighted 4:4:4 sampling format distortion calculation method in the rate-distortion optimization process,finally 4:4:4 sampling format in-loop filtering and offset algorithms were applied to the 4:4:4 sampling format image after up sampling.The experimental results show that,for full-chroma natural images,the MCFCC algorithm achieves higher coding efficiency at very low additional encoding complexity and very low additional design and implementation cost.…”
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Dual-Branch Neural Network-Based In-Loop Filter for VVC Intra Coding Using Spatial-Frequency Feature Fusion by Zhen Feng, Xu Liu, Cheolkon Jung

“…In company with the reconstructed frame, the quantization parameter (QP) map, the predicted frame, and the partition frame are popularly used as auxiliary input for the neural network-based in-loop filter (NNLF). …”
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High-speed railway track components inspection framework based on YOLOv8 with high-performance model deployment by Youzhi Tang, Yu Qian

“…Our research introduces an efficient model inference pipeline based on a producer-consumer model, effectively utilizing parallel processing and concurrent computing to enhance performance. …”
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A Configurable Accelerator for CNN-Based Remote Sensing Object Detection on FPGAs by Yingzhao Shao, Jincheng Shang, Yunsong Li, Yueli Ding, Mingming Zhang, Ke Ren, Yang Liu

“…This paper proposes a new generation of high flexibility and intelligent CNNs hardware accelerator for satellite remote sensing in order to make its computing carrier more lightweight and efficient. A data quantization scheme for INT16 or INT8 is designed based on the idea of dynamic fixed point numbers and is applied to different scenarios. …”
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Lightweight Deep Learning Model for Fire Classification in Tunnels by Shakhnoza Muksimova, Sabina Umirzakova, Jushkin Baltayev, Young-Im Cho

“…Deployment optimizations, such as quantization and layer fusion, ensure computational efficiency, achieving an average inference time of 12ms/frame, making it suitable for resource-constrained environments like IoT and edge devices. …”
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A Comparative Analysis of Compression and Transfer Learning Techniques in DeepFake Detection Models by Andreas Karathanasis, John Violos, Ioannis Kompatsiaris

“…Pruning, knowledge distillation, quantization, and adapter modules are explored to enable efficient real-time DeepFake detection. …”
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Pruning and optimization of optical neural network as a binary optical trigger by Bokun Zhao, Xuening Dong, Kaveh Rahbardar Mojaver, Brett H. Meyer, Odile Liboiron-Ladouceur

“…Optical neural networks implemented with Mach-Zehnder Interferometer (MZI) arrays are a promising solution to enable fast and energy-efficient machine learning inference, yet finding a practical application has proven challenging due to sensitivity to thermal noise and loss. …”
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High-Bit-Depth Geometry Representation and Compression in MPEG Immersive Video System by Yoonseob Lee, Kwan-Jung Oh, Gwangsoon Lee, Byung Tae Oh

“…Immersive video has been rapidly evolving as a new form of media for virtual reality (VR) applications, increasing demand for efficient compression and transmission techniques. …”
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Hardware Implementation of Block Floating-Point FFT Based on Approximate Computation and Conflict-Free Access by Xiaohui Zhang, Zenghui Yu, Shushan Qiao, Yi Zhan

“…Operating at a frequency of 671.14 MHz, it delivers a throughput of 1073.82 MS/s for 4096-point FFT computations, with a power consumption of only 110.85 mW and a Signal–to–Quantization Noise Ratio of 58.51 dB. Compared to state-of-the-art designs, this architecture achieves breakthrough improvements in throughput performance while maintaining competitive energy efficiency.…”
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RDM-YOLO: A Lightweight Multi-Scale Model for Real-Time Behavior Recognition of Fourth Instar Silkworms in Sericulture by Jinye Gao, Jun Sun, Xiaohong Wu, Chunxia Dai

“…Second, standard convolutional layers are replaced with distribution shifting convolution (DSConv), leveraging dynamic sparsity and quantization mechanisms to reduce computational complexity. …”
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High-Fold 3D Gaussian Splatting Model Pruning Method Assisted by Opacity by Shiyu Qiu, Chunlei Wu, Zhenghao Wan, Siyuan Tong

“…To address this, 3D Gaussian Splatting (3DGS) has emerged as an efficient alternative, utilizing Gaussian-based representations and rasterization techniques to achieve faster rendering speeds without sacrificing image quality. …”
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