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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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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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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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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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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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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Edge AI for Real-Time Anomaly Detection in Smart Homes by Manuel J. C. S. Reis, Carlos Serôdio

“…The increasing adoption of smart home technologies has intensified the demand for real-time anomaly detection to improve security, energy efficiency, and device reliability. Traditional cloud-based approaches introduce latency, privacy concerns, and network dependency, making Edge AI a compelling alternative for low-latency, on-device processing. …”
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Optimizing encrypted search in the cloud using autoencoder-based query approximation by Mahmoud Mohamed, Khaled Alosman

“…However, encryption reduces search efficiency due to inability to directly compute on ciphertexts. …”
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Benchmarking In-Sensor Machine Learning Computing: An Extension to the MLCommons-Tiny Suite by Fabrizio Maria Aymone, Danilo Pietro Pau

“…With the exponential growth of edge devices, efficient local processing is essential to mitigate economic costs, latency, and privacy concerns associated with the centralized cloud processing. …”
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A seed-epidermis-feature-recognition-based lightweight peanut seed selection method for embedded systems by Dehao Li, Jinlong Huang, Xincheng Li, Zhaolei Yang, Xueke An, Pengfei Xu, Yuliang Yun

“…The model was based on deep learning and model quantization techniques. The transfer learning method was used to use four pre-trained models, EfficientNet_b0, EfficientNetv2-b0, MobileNet_v2_35_224, and NasNet_Mobile, as feature extraction layers, the input layer was added before the feature extraction layer, and the dropout and dense layers were added after the feature extraction layer to construct a classifier. …”
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Anatomy of Deep Learning Image Classification and Object Detection on Commercial Edge Devices: A Case Study on Face Mask Detection by Dimitrios Kolosov, Vasilios Kelefouras, Pandelis Kourtessis, Iosif Mporas

“…To leverage the computational power of the edge devices, the models have been optimized, first, by using the SOTA optimization frameworks (TensorFlow Lite, OpenVINO, TensorRT, eIQ) and, second, by evaluating/comparing different optimization options, e.g., different levels of quantization. Note that the five edge devices are evaluated and compared too, in terms of inference time, value and efficiency. …”
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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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A Comparative Study and Optimization of Camera-Based BEV Segmentation for Real-Time Autonomous Driving by Woomin Jun, Sungjin Lee

“…Notably, the lift–splat–shoot view transformation model with the InternImage-T encoder and EfficientNet-B0 decoder demonstrated performance of 53.1 mIoU while achieving high efficiency (51.7 ms and 159.5 MB, respectively). …”
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Using Value Stream Maps To Treatment Waste Case study in Karbala Holy Health Department by Mohanad Hameed Al-Atwi, Fadhil Abbas Musa Al-Radhi

“…Some appropriate quantitative methods were used (total time to add value, total time not added value, service efficiency, improvement rate). …”
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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

“…This study presents RDM-YOLO, a computationally efficient deep learning framework derived from YOLOv5s architecture, specifically designed for the automated detection of three essential behaviors (resting, wriggling, and eating) in fourth instar silkworms. …”
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ConflLlama: Domain-specific adaptation of large language models for conflict event classification by Shreyas Meher, Patrick T. Brandt

“…We demonstrate how to adapt open-source language models to specialized political science tasks, using conflict event classification as our proof of concept. Through quantization and efficient fine-tuning techniques, we show state-of-the-art performance while minimizing computational requirements. …”
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Retracted: Computer Medical Image Segmentation Based on Neural Network by Xiaopeng Wang, Lei Gu, Zhongyi Wang

“…Finally, we propose an FPGA-based multilevel optimization architecture for energy-efficient cellular neural networks. The optimization scheme includes three levels: system level, module level, and design space. …”
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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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BinaryViT: Binary Vision Transformer for Hyperspectral Image Classification by Xiang Hu, Taolin Liu, Zhe Guo, Yuxiang Tang, Yuanxi Peng, Tong Zhou

“…However, binary quantization in transformers faces challenges such as degradation of feature representation capability after binarizing self-attention mechanisms and decline in fusion efficiency of multiscale spectral–spatial information, leading to relatively lagging progress in this field. …”
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The influence of digital transformation on the total factor productivity of enterprises: the intermediate role of human-machine cooperation by Qi Xiong, Jingyi Yang, Xiuwu Zhang, Yarui Deng, Yao Gui, Xiaoyang Guo

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