Yuejiao Wang, Zhong Ma^*, Chaojie Yang, Yu Yang, Lu Wei

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 819-836, 2024, DOI:10.32604/cmc.2024.047108

Abstract The quantization algorithm compresses the original network by reducing the numerical bit width of the model, which improves the computation speed. Because different layers have different redundancy and sensitivity to data bit width. Reducing the data bit width will result in a loss of accuracy. Therefore, it is difficult to determine the optimal bit width for different parts of the network with guaranteed accuracy. Mixed precision quantization can effectively reduce the amount of computation while keeping the model accuracy basically unchanged. In this paper, a hardware-aware mixed precision quantization strategy optimal assignment algorithm adapted to low bit width is proposed,… More >

Tuo Ma, Zhiwei Li, Qingjiang Li^*, Haijun Liu, Zhongjin Zhao, Yinan Wang

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3237-3263, 2023, DOI:10.32604/cmc.2023.045948

Abstract With the continuous development of deep learning, Deep Convolutional Neural Network (DCNN) has attracted wide attention in the industry due to its high accuracy in image classification. Compared with other DCNN hardware deployment platforms, Field Programmable Gate Array (FPGA) has the advantages of being programmable, low power consumption, parallelism, and low cost. However, the enormous amount of calculation of DCNN and the limited logic capacity of FPGA restrict the energy efficiency of the DCNN accelerator. The traditional sequential sliding window method can improve the throughput of the DCNN accelerator by data multiplexing, but this method’s data multiplexing rate is low… More >

Lu Wei, Zhong Ma^*, Chaojie Yang

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 981-1000, 2024, DOI:10.32604/cmes.2023.027085

Abstract The demand for adopting neural networks in resource-constrained embedded devices is continuously increasing. Quantization is one of the most promising solutions to reduce computational cost and memory storage on embedded devices. In order to reduce the complexity and overhead of deploying neural networks on Integer-only hardware, most current quantization methods use a symmetric quantization mapping strategy to quantize a floating-point neural network into an integer network. However, although symmetric quantization has the advantage of easier implementation, it is sub-optimal for cases where the range could be skewed and not symmetric. This often comes at the cost of lower accuracy. This… More > Graphic Abstract

Zerui Zhen¹, Zihao Wu², Lei Feng^1,*, Wenjing Li¹, Feng Qi¹, Shixuan Guo¹

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2939-2955, 2023, DOI:10.32604/cmc.2023.036505

Abstract Asynchronous federated learning (AsynFL) can effectively mitigate the impact of heterogeneity of edge nodes on joint training while satisfying participant user privacy protection and data security. However, the frequent exchange of massive data can lead to excess communication overhead between edge and central nodes regardless of whether the federated learning (FL) algorithm uses synchronous or asynchronous aggregation. Therefore, there is an urgent need for a method that can simultaneously take into account device heterogeneity and edge node energy consumption reduction. This paper proposes a novel Fixed-point Asynchronous Federated Learning (FixedAsynFL) algorithm, which could mitigate the resource consumption caused by frequent… More >

Adepu Shravan Kumar, S. Srinivasan^*

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3607-3620, 2023, DOI:10.32604/iasc.2023.036647

Abstract At the present time, the Industrial Internet of Things (IIoT) has swiftly evolved and emerged, and picture data that is collected by terminal devices or IoT nodes are tied to the user's private data. The use of image sensors as an automation tool for the IIoT is increasingly becoming more common. Due to the fact that this organisation transfers an enormous number of photographs at any one time, one of the most significant issues that it has is reducing the total quantity of data that is sent and, as a result, the available bandwidth, without compromising the image quality. Image… More >

Xiaojie Zhao¹, Xiankui Meng¹, Ruyong Ren², Shaozhang Niu^2,*, Zhenguang Gao³

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1765-1781, 2023, DOI:10.32604/cmc.2023.029552

Abstract Detecting double Joint Photographic Experts Group (JPEG) compression for color images is vital in the field of image forensics. In previous researches, there have been various approaches to detecting double JPEG compression with different quantization matrices. However, the detection of double JPEG color images with the same quantization matrix is still a challenging task. An effective detection approach to extract features is proposed in this paper by combining traditional analysis with Convolutional Neural Networks (CNN). On the one hand, the number of nonzero pixels and the sum of pixel values of color space conversion error are provided with 12-dimensional features… More >

A. Arokiaraj Jovith¹, S. Rama Sree², Gudikandhula Narasimha Rao³, K. Vijaya Kumar⁴, Woong Cho⁵, Gyanendra Prasad Joshi⁶, Sung Won Kim^7,*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6429-6444, 2023, DOI:10.32604/cmc.2023.031817

Abstract The exponential growth of data necessitates an effective data storage scheme, which helps to effectively manage the large quantity of data. To accomplish this, Deoxyribonucleic Acid (DNA) digital data storage process can be employed, which encodes and decodes binary data to and from synthesized strands of DNA. Vector quantization (VQ) is a commonly employed scheme for image compression and the optimal codebook generation is an effective process to reach maximum compression efficiency. This article introduces a new DNA Computing with Water Strider Algorithm based Vector Quantization (DNAC-WSAVQ) technique for Data Storage Systems. The proposed DNAC-WSAVQ technique enables encoding data using… More >

Soonshin Seo¹, Junseok Oh², Eunsoo Cho², Hosung Park², Gyujin Kim², Ji-Hwan Kim^2,*

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3291-3303, 2022, DOI:10.32604/cmc.2022.026259

Abstract Acoustic scene classification (ASC) is a method of recognizing and classifying environments that employ acoustic signals. Various ASC approaches based on deep learning have been developed, with convolutional neural networks (CNNs) proving to be the most reliable and commonly utilized in ASC systems due to their suitability for constructing lightweight models. When using ASC systems in the real world, model complexity and device robustness are essential considerations. In this paper, we propose a two-pass mobile network for low-complexity classification of the acoustic scene, named TP-MobNet. With inverse residuals and linear bottlenecks, TP-MobNet is based on MobileNetV2, and following mobile blocks,… More >

Krishan Kumar^1,*, Mohamed Abouhawwash^2,3, Amit Kant Pandit¹, Shubham Mahajan¹, Mofreh A. Hogo⁴

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1977-1993, 2022, DOI:10.32604/cmc.2022.027850

Abstract The high-efficiency video coder (HEVC) is one of the most advanced techniques used in growing real-time multimedia applications today. However, they require large bandwidth for transmission through bandwidth, and bandwidth varies with different video sequences/formats. This paper proposes an adaptive information-based variable quantization matrix (AI-VQM) developed for different video formats having variable energy levels. The quantization method is adapted based on video sequence using statistical analysis, improving bit budget, quality and complexity reduction. Further, to have precise control over bit rate and quality, a multi-constraint prune algorithm is proposed in the second stage of the AI-VQM technique for pre-calculating K… More >

M. Saravanan¹, J. Jayanthi², U. Sakthi³, R. Rajkumar⁴, Gyanendra Prasad Joshi⁵, L. Minh Dang⁵, Hyeonjoon Moon^5,*

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2683-2696, 2022, DOI:10.32604/cmc.2022.025642

Abstract Due to latest advancements in the field of remote sensing, it becomes easier to acquire high quality images by the use of various satellites along with the sensing components. But the massive quantity of data poses a challenging issue to store and effectively transmit the remote sensing images. Therefore, image compression techniques can be utilized to process remote sensing images. In this aspect, vector quantization (VQ) can be employed for image compression and the widely applied VQ approach is Linde–Buzo–Gray (LBG) which creates a local optimum codebook for image construction. The process of constructing the codebook can be treated as… More >