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Accelerating Convolutional processing by Harnessing Channel Shifts in Arrayed Waveguide Gratings

LASER & PHOTONICS REVIEWS 2025年第1期19卷

作者： Yi, Dan Zhao, Caiyue Zhang, Zunyue Xu, Hongnan Tsang, Hon Ki Chinese Univ Hong Kong Dept Elect Engn Shatin Hong Kong Peoples R China Tianjin Univ Sch Precis Instrument & Optoelect Engn Tianjin 300072 Peoples R China

Convolutional neural networks are a powerful category of artificial neural networks that can extract features from raw data to provide greatly reduced parametric complexity and enhance pattern recognition and the accuracy of prediction. Optical neural networks offer the promise of dramatically accelerating computing speed while maintaining low power consumption even when using high-speed data streams running at hundreds of gigabit/s. Here, we propose an optical convolutional processor (CP) that leverages the spectral response of an arrayed waveguide grating (AWG) to enhance convolution speed by eliminating the need for repetitive element-wise multiplication. Our design features a balanced AWG configuration, enabling both positive and negative weightings essential for convolutional kernels. A proof-of-concept demonstration of an 8-bit resolution processor is experimentally implemented using a pair of AWGs with a broadband Mach-Zehnder interferometer (MZI) designed to achieve uniform weighting across the whole spectrum. Experimental results demonstrate the CP's effectiveness in edge detection and achieved 96% accuracy in a convolutional neural network for MNIST recognition. This approach can be extended to other common operations, such as pooling and deconvolution in Generative Adversarial networks. It is also scalable to more complex networks, making it suitable for applications like autonomous vehicles and real-time video recognition. A novel convolutional processor is proposed using the shifted spectral response of a pair of arrayed waveguide gratings (AWGs) to mimic the kernel shifts during image convolution. This inherent mixing of inputs in the AWG's spectral response eliminates the need for repetitive element-wise computations while enabling the simultaneous generation of convolved output maps. image

关键词： array waveguide grating convolutional processor deep learning image processing optical computing

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Design and Analysis of Energy Efficient Approximate Multipliers for image processing and Deep neural Network

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2025年第2期72卷 854-867页

作者： Kumari, Anupam Palathinkal, Roy Paily Indian Inst Technol Guwahati IIT Guwahati Dept Elect & Elect Engn Gauhati 781039 India

Numerous obstacles in enhancing the performance of computing systems have spurred the emergence of approximate computing. Extensive studies have been reported on approximate computing to develop high-performance, energy-efficient hardware designs tailored to error-resilient applications. In this brief, we proposed 8-bit approximate multipliers with 15 levels of accuracy using three techniques: recursive, bit-wise, and hybrid approximation using partial bit OR (PBO). Compared to the existing multipliers, investigated designs have significantly improved the area, power, delay, Power Delay Product (PDP), and Power Area Delay Product (PADP) by 41.68%, 73.16%, 35.57%, 72.65%, and 75.42% respectively on average. On resemblance with the accurate multiplier, the area, power, delay, PDP, and PADP were enhanced by 54.41%, 57.57%, 25.73%, 60.14%, and 74.33% correspondingly on average. Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) values surpassing (30 dB, 94%), (31 dB, 96%), and (26 dB, 95%) by applying them to benchmarks in image smoothing, edge detection, and image sharpening successively. Moreover, upon scrutinizing the efficacy of multipliers in hardware implementations of deep neural networks attaining the performance exceeding 95%. The obtained results confirm that suggested multipliers are well-suited for their widespread applications.

关键词： Delays Hardware Circuits Accuracy Logic gates Adders Approximate computing Energy efficiency image edge detection artificial neural networks approximate multiplier energy efficient image processing neural network

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applications of Pruning Methods in Natural Language processing

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IEEE ACCESS 2024年 12卷 89418-89438页

作者： Touheed, Marva Zubair, Urooj Sabir, Dilshad Hassan, Ali Butt, Muhammad Fasih Uddin Riaz, Farhan Abdul, Wadood Ayub, Rashid COMSATS Univ Islamabad Dept Elect & Comp Engn Islamabad 45550 Pakistan Natl Univ Sci & Technol Coll Elect & Mech Engn Dept Comp & Software Engn Islamabad 44000 Pakistan COMSATS Univ Islamabad Next Generat Commun Res Grp Islamabad 45550 Pakistan Univ Lincoln Coll Hlth & Sci Sch Comp Sci Lincoln LN6 7DL England King Saud Univ Coll Comp & Informat Sci Dept Comp Engn Riyadh 11543 Saudi Arabia King Saud Univ Coll Comp & Informat Sci Dept Sci Technol & Innovat Riyadh 11543 Saudi Arabia

Deep neural networks (DNN) are in high demand because of their widespread applications in natural language processing, image processing, and a lot of other domains. However, due to their computational expense, over-parameterization, and large memory requirements, DNN applications often require the use of substantial model resources. This strict requirement of latency and limited memory availability are hurdles in the device deployment of these technologies. Therefore, a common idea could be to mitigate the DNN-based models' size without any performance degradation using different compression techniques. During the last few years, a great deal of progress has been made in the field of Natural Language processing (NLP) using deep learning approaches. The objective of this research is to offer a thorough overview of the various pruning methods applied in the context of NLP. In this paper, we review several recent pruning-based schemes used for converting standard networks into their compact and accelerated versions. Traditionally, pruning is a technique for improving latency, reducing model size, and computational complexity which is a viable approach to deal with the above-mentioned challenges. In general, these techniques are divided into two main categories: structural and unstructured pruning methods. Structural pruning methods are further classified into filter, channel, layer, block, and movement pruning. Whereas, neuron, magnitude-based, and iterative pruning lie in the category of unstructured pruning. For each method, we discuss the related metrics and benchmarks. Then recent work on each method is discussed in detail, which provides insightful analysis of the performance, related applications, and pros and cons. Then, a comparative analysis is provided to analyze the differences among approaches. Finally, the paper concludes with possible future directions and some technical challenges.

关键词： Computational modeling Data models Task analysis neural networks Training Sentiment analysis Natural language processing Convolutional neural networks artificial neural networks Pruning convolution neural networks natural language processing DNN model compression acceleration

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An optimized capsule neural networks for tomato leaf disease classification

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EURASIP JOURNAL ON image AND VIDEO processing 2024年第1期2024卷 2页

作者： Abouelmagd, Lobna M. Shams, Mahmoud Y. Marie, Hanaa Salem Hassanien, Aboul Ella Misr Higher Inst Commerce & Comp Mansoura Egypt Kafrelsheikh Univ Fac Artificial Intelligence Kafr Al Sheikh 33516 Egypt Delta Univ Sci & Technol Fac Artificial Intelligence Gamasa 35712 Egypt Cairo Univ Fac Comp & Artificial Intelligence Cairo Egypt

Plant diseases have a significant impact on leaves, with each disease exhibiting specific spots characterized by unique colors and locations. Therefore, it is crucial to develop a method for detecting these diseases based on spot shape, color, and location within the leaves. While Convolutional neural networks (CNNs) have been widely used in deep learning applications, they suffer from limitations in capturing relative spatial and orientation relationships. This paper presents a computer vision methodology that utilizes an optimized capsule neural network (CapsNet) to detect and classify ten tomato leaf diseases using standard dataset images. To mitigate overfitting, data augmentation, and preprocessing techniques were employed during the training phase. CapsNet was chosen over CNNs due to its superior ability to capture spatial positioning within the image. The proposed CapsNet approach achieved an accuracy of 96.39% with minimal loss, relying on a 0.00001 Adam optimizer. By comparing the results with existing state-of-the-art approaches, the study demonstrates the effectiveness of CapsNet in accurately identifying and classifying tomato leaf diseases based on spot shape, color, and location. The findings highlight the potential of CapsNet as an alternative to CNNs for improving disease detection and classification in plant pathology research. This paper is interested in tracking plant diseases such as leaf disease in Tomatoes, based on optimized Deep Learning Capsule *** detection of the most common diseases based on leaf images can help us track and enhance food *** networks is an optimized alternative method more efficient than traditional CNN for detecting all leaf images and determining all locations. However, it tackles the shortage of conventional CNN.

关键词： Drone Tomato leaf diseases Convolutional neural networks Capsule neural networks Adam optimizer

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DiffStyler: Controllable Dual Diffusion for Text-Driven image Stylization

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IEEE TRANSACTIONS ON neural networks AND LEARNING SYSTEMS 2025年第2期36卷 3370-3383页

作者： Huang, Nisha Zhang, Yuxin Tang, Fan Ma, Chongyang Huang, Haibin Dong, Weiming Xu, Changsheng Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Comp Technol Beijing 100190 Peoples R China Kuaishou Technol Beijing 100085 Peoples R China

Despite the impressive results of arbitrary image-guided style transfer methods, text-driven image stylization has recently been proposed for transferring a natural image into a stylized one according to textual descriptions of the target style provided by the user. Unlike the previous image-to-image transfer approaches, text-guided stylization progress provides users with a more precise and intuitive way to express the desired style. However, the huge discrepancy between cross-modal inputs/outputs makes it challenging to conduct text-driven image stylization in a typical feed-forward CNN pipeline. In this article, we present DiffStyler, a dual diffusion processing architecture to control the balance between the content and style of the diffused results. The cross-modal style information can be easily integrated as guidance during the diffusion process step-by-step. Furthermore, we propose a content image-based learnable noise on which the reverse denoising process is based, enabling the stylization results to better preserve the structure information of the content image. We validate the proposed DiffStyler beyond the baseline methods through extensive qualitative and quantitative experiments. The code is available at https://***/haha-lisa/Diffstyler.

关键词： Arbitrary image stylization diffusion textual guidance neural network applications

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A hardware accelerator to support deep learning processor units in real-time image processing

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ENGINEERING applications OF artificial INTELLIGENCE 2025年 145卷

作者： Cittadini, Edoardo Marinoni, Mauro Buttazzo, Giorgio Scuola Super Sant Anna Pisa Italy

Deep neural networks are becoming crucial in many cyber-physical systems involving complex perceptual tasks. For those embedded systems requiring real-time interactions with dynamic environments, as autonomous robots and drones, it is of paramount importance that such algorithms are efficiently executed onboard on properly designed hardware accelerators to meet the required performance specifications. In particular, some neural network architectures for object detection and tracking, as You Only Look Once (YOLO), include heavy computational stages that need to be executed before and after the model inference. Such stages are typically not incorporated in traditional accelerators and are executed on general-purpose processors, thus introducing a bottleneck in the overall processing pipeline. To overcome such a problem, this paper presents a generalpurpose accelerator on a field-programmable gate array (FPGA) able to run pre-processing and post-processing operations typically required by vision tasks. The proposed solution has been tested in combination with a YOLO object detector accelerated on an Advanced Micro Devices (AMD) Xilinx Kria KR260 board mounting an UltraScale+ multiprocessor system-on-chip, achieving a significant improvement in terms of both timing performance and power consumption, and enabling onboard visual processing into drones. The proposed solution is able to boost the traditional object detection process by a factor of 4.4, allowing the execution of the full processing pipeline at 60 frames per second (fps), versus 13.6 fps reachable without the proposed accelerator. Asa result, this work enables the use of high-speed cameras for developing more reactive systems that can respond to incoming events with lower latency.

关键词： Hardware acceleration Object detection Deep neural networks Real-time processing image pre-processing acceleration Deep processing unit

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Event-Based Spiking neural networks for Object Detection: A Review of Datasets, Architectures, Learning Rules, and Implementation

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IEEE ACCESS 2024年 12卷 180532-180596页

作者： Iaboni, Craig Abichandani, Pramod New Jersey Inst Technol Ying Wu Coll Comp YWCC Newark NJ 07103 USA New Jersey Inst Technol Newark Coll Engn NCE Robot & Data Lab RADLab Newark NJ 07103 USA

Spiking neural networks (SNNs) represent a biologically inspired paradigm offering an energy-efficient alternative to conventional artificial neural networks (ANNs) for Computer Vision (CV) applications. This paper presents a systematic review of datasets, architectures, learning methods, implementation techniques, and evaluation methodologies used in CV-based object detection tasks using SNNs. Based on an analysis of 151 journal and conference articles, the review codifies: 1) the effectiveness of fully connected, convolutional, and recurrent architectures;2) the performance of direct unsupervised, direct supervised, and indirect learning methods;and 3) the trade-offs in energy consumption, latency, and memory in neuromorphic hardware implementations. An open-source repository along with detailed examples of Python code and resources for building SNN models, event-based data processing, and SNN simulations are provided. Key challenges in SNN training, hardware integration, and future directions for CV applications are also identified.

关键词： Computer architecture Neuromorphics Hardware Computer vision Neurons Encoding Learning systems Biological information theory Spiking neural networks Feature extraction Object detection Cameras Event detection Energy efficiency object detection neuromorphic hardware event cameras

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Advanced deep learning algorithms in oral cancer detection: Techniques and applications

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JOURNAL OF ENVIRONMENTAL SCIENCE AND HEALTH PART C-TOXICOLOGY AND CARCINOGENESIS 2025年第2期43卷 133-158页

作者： Wankhade, Dipali Dhawale, Chitra Meshram, Mrunal Datta Meghe Inst Higher Educ & Res Wardha Nagpur Nagpur India Datta Meghe Inst Higher Educ & Res Deemed Univ Fac Sci & Technol Wardha India Sharad Pawar Dent Collage Dept Oral Med & Radiol Wardha India

As the 16th most common cancer globally, oral cancer yearly accounts for some 355,000 new cases. This study underlines that an early diagnosis can improve the prognosis and cut down on mortality. It discloses a multifaceted approach to the detection of oral cancer, including clinical examination, biopsies, imaging techniques, and the incorporation of artificial intelligence and deep learning methods. This study is distinctive in that it provides a thorough analysis of the most recent AI-based methods for detecting oral cancer, including deep learning models and machine learning algorithms that use convolutional neural networks. By improving the precision and effectiveness of cancer cell detection, these models eventually make early diagnosis and therapy possible. This study also discusses the importance of techniques in image pre-processing and segmentation in improving image quality and feature extraction, an essential component of accurate diagnosis. These techniques have shown promising results, with classification accuracies reaching up to 97.66% in some models. Integrating the conventional methods with the cutting-edge AI technologies, this study seeks to advance early diagnosis of oral cancer, thus enhancing patient outcomes and cutting down on the burden this disease is imposing on healthcare systems.

关键词： Oral cancer detection artificial intelligence deep learning convolutional neural networks machine learning early diagnosis medical imaging image processing oral squamous cell carcinoma predictive modeling

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Estimating Water Levels through Smartphone-imaged Gauges: A Comparative Analysis of ANN, DL, and CNN Models

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WATER RESOURCES MANAGEMENT 2025年第4期39卷 1639-1654页

作者： Santos, Celso Augusto Guimaraes Ghorbani, Mohammad Ali Abdi, Erfan Patel, Utkarsh Sadeddin, Siria Univ Fed Paraiba Dept Civil & Environm Engn BR-58051900 Joao Pessoa PB Brazil Univ Tabriz Dept Water Engn Tabriz Iran Indian Inst Technol IIT Bhilai Main Campus Durg 491001 Chhattisgarh India Univ Nacl Colombia Math Dept Bogota Colombia

Understanding and monitoring water levels are essential for various applications, including environmental protection, public safety, and resource management. Water level estimation, a critical aspect of hydrological monitoring, is often constrained by challenges such as resource scarcity, high costs, and time-intensive processes. This research addresses these limitations by developing a machine learning-based system for automatic and real-time water level control. Specifically, it investigates the effectiveness of a non-contact, image-based water level measurement approach, leveraging recent advancements in mobile imaging technology. images were captured using a standard smartphone equipped with an RGB camera for water level analysis. Through precise image alignment processing under both clear and turbid conditions, the water's edge on a gauge was accurately detected. The study centers on the development and comparison of three computational models: artificial neural networks (ANN), Deep Learning (DL), and Convolutional neural networks (CNN). These models were trained to estimate water levels from processed image data. Results demonstrated varying levels of accuracy across models, with the CNN model outperforming others, achieving the lowest error rate of 24.36 mm and the highest correlation of 0.986. In contrast, the ANN model yielded the highest error rate at 30.76 mm and the lowest correlation of 0.968, highlighting the relative effectiveness of CNN in this application. Given the high accuracy (92.6%) of the image processing method and CNN model in detecting water surface edges and determining water levels, this system has substantial potential to enhance water resource management and control efficiency.

关键词： Computational Hydrology Convolutional neural networks Hydrological Monitoring image Analysis Non-contact Measurement

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Improved spectral filtering of broadband diffractive neural network by loss function engineering

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APPLIED PHYSICS LETTERS 2025年第8期126卷

作者： Li, Bolin Luan, Guangrui Zhu, Yinfei Fei, Jinlei Gu, Min Lin, Jian Univ Shanghai Sci & Technol Sch Artificial Intelligence Sci & Technol Shanghai Peoples R China Univ Shanghai Sci & Technol Inst Photon Chips Shanghai Peoples R China

We engineer the loss function by removing the conventional physics-based energy constraint during the training of broadband diffractive neural networks (DNNs) to enhance their spectral filtering capabilities of supercontinuum light. Simulations show that compared to DNNs trained with conventional loss function, the suppression of out-of-band spectral intensities can be improved by three orders of magnitude, resulting in an extinction coefficient of 10(-6). Additionally, the spectral resolution can be enhanced by over 50% with a 6.6% improvement of energy efficiency. These findings are corroborated by experiments conducted with a two-layer DNN. The proposed method holds promise for enhancing the performance of broadband DNNs across various applications, including spectral reconstruction, spectrum classification, and color image processing, among others.

关键词： Color image processing

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