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Proceedings of the 6th International Conference on Compiler Construction, CC'96

作者： Janssen, Johan Corporaal, Henk Delft University of Technology Department of Electrical Engineering Section Computer Architecture and Digital Systems 2600 GA Delft 5031 Netherlands

ISBN: (纸本)9783540610533

To exploit instruction level parallelism in programs over multiple basic blocks, programs should have reducible control flow graphs. However not all programs satisfy this property. A new method, called Controlled Node Splitting (CNS), for transforming irreducible control flow graphs to reducible control flow graphs is presented. CNS duplicates nodes of the control flow graph to obtain reducible control flow graphs. CNS results in a minimum number of splits and a minimum number of duplicates. Since the computation time to find the optimal split sequence is large, a heuristic has been developed. The results of this heuristic are close to the optimum. Straightforward application of node splitting may result in an average code size increase of 235%. CNS with the heuristic limits the increase to only 3%. © Springer-Verlag Berlin Heidelberg 1996.

关键词： Compilation Control flow graphs Instruction level parallelism Irreducibility Node splitting Reducibility

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A quantum-classical hybrid architecture for security algorithms acceleration

A quantum-classical hybrid architecture for security algorit...

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11th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom-2012

作者： Possignolo, Rafael Trapani Margi, Cíntia Borges Department of Computer and Digital Systems Engineering Escola Politécnica University of São Paulo São Paulo Brazil

ISBN: (纸本)9780769547459

Since the discovery of Shor's algorithm, the anxiety about quantum computation has increased. A large amount of research has been conducted to discover new algorithms and to build a quantum computer. But it seems that a general purpose quantum computer is far from being achieved. Meanwhile, cryptographers around the world started to look for security algorithms that resist to quantum attacks, but these still need improvement to achieve practical execution time. This work proposes a quantum-classical hybrid architecture, focusing on photonic quantum computers. A small quantum coprocessor implementing the Grover search algorithm is used to perform the search for roots of polynomials in double-struck Fpq. This coprocessor is used to accelerate the decoding process of the McEliece cryptosystem. © 2012 IEEE.

关键词： Quantum computers

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Power-aware and reliable sensor selection based on trust for Wireless Sensor Networks

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Journal of Communications 2010年第1期5卷 23-30页

作者： Han, Guangjie Shu, Lei Ma, Jianhua Park, Jong Hyuk Ni, Jianjun Department of Information and Communication Systems Hohai University Changzhou China Digital Enterprise Research Institute National University of Ireland Galway Ireland Department of Multimedia Engineering Osaka University Japan Faculty of Computer and Information Sciences Hosei University Japan Department of Computer Science and Engineering Seoul National University of Technology Korea Republic of

Wireless Sensor Networks (WSNs) is temporarily formed, operated and managed by the nodes themselves. In a complex WSN, malicious nodes are well disguised, and they can attack the entire network on specific purpose by utilizing the natural cooperation of nodes. The error-prone characteristic of sensor nodes can also cause instability in WSN. Therefore, how to choose one or more suitable sensor nodes to collaborate towards a better system performance is a critical issue. In this paper, we construct a new trust model for WSNs based on the trust mechanism in human society. Based on this trust model, we propose a novel power-aware and reliable scheme for sensor selection (PRS). The remaining energy of a node can be considered as an important restrictive factor for the proposed scheme. Simulation results show that PRS scheme can improve the system stability, defend against the attacks of malicious nodes, and prolong the lifetime of WSNs, effectively. © 2010 Academy Publisher.

关键词： Sensor nodes

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A Lightweight CNN Model for Tomato Crop Diseases on Heterogeneous Embedded System 12

A Lightweight CNN Model for Tomato Crop Diseases on Heteroge...

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12th International Conference on Modern Circuits and systems Technologies, MOCAST 2023

作者： Sanida, Theodora Sanida, Maria Vasiliki Sideris, Argyrios Dasygenis, Minas University of Western Macedonia Department of Electrical & Computer Engineering Kozani50100 Greece University of Piraeus Department of Digital Systems Piraeus18534 Greece

ISBN: (纸本)9798350321074

Tomato plants are vulnerable to several diseases, each of which may cause severe harm to the plant. These adverse conditions can significantly reduce the amount and quality of agricultural yields. In crop disease diagnostics, convolutional neural network (CNN) algorithms have recently materialised as the most critical area of research. Therefore, this work aims to develop a robust and lightweight CNN-based diagnostic tool for tomato leaf tissue diseases in a low-power heterogeneous embedded device. The research data consists of one category of healthy tomatoes obtained from PlantVillage and nine diseases that may affect them. We evaluated the effectiveness of the lightweight CNN diagnostic tool using conventional identification criteria, including accuracy, precision, area under the curve (AUC), f1-score, and recall, on 3,635 previously unseen test images. The outcomes are more than satisfactory, with 99.04% accuracy, 99.56% AUC, 99.00% f1-score, 98.78% precision, and 99.23% recall, respectively. The developed CNN-based diagnostic tool represents a significant advance in tomato leaf disease diagnosis, paving the way for future research to develop lightweight networks in low-power embedded devices for rapid and accurate diagnosis of plant diseases, benefiting farmers. © 2023 IEEE.

关键词： Convolutional neural networks

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An Efficiency CNN Solution for Olive Disease Management Through FPGA 8

An Efficiency CNN Solution for Olive Disease Management Thro...

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8th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2023

作者： Sanida, Theodora Sideris, Argyrios Sanida, Maria Vasiliki Dossis, Michael Dasygenis, Minas University of Western Macedonia Department of Electrical & Computer Engineering Kozani50131 Greece University of Piraeus Department of Digital Systems Piraeus18534 Greece

ISBN: (纸本)9798350383676

Olives, one of the world's oldest and most significant cultivated crops, face persistent threats from various diseases, devastatingly affecting olive production and quality. In this era of technological advancement, the integration of Convolutional Neural Networks (CNNs) has emerged as a promising tool for the early and accurate identification of diseases in agricultural crops. This work presents a novel solution of CNNs for the automated identification of diseases in olive plants in 3 categories (aculus olearius healthy and olive peacock spot). To further enhance the practicality and real-time applicability of the proposed system, an implementation of Field-Programmable Gate Arrays (FPGAs) is explored. This study delves into the FPGA-based deployment of the CNN solution, optimizing its computational efficiency for on-site disease diagnosis with a 98.82% accuracy rate. The results of this study reveal the potential of CNNs in revolutionizing olive disease management, enabling early intervention and precise treatment. Additionally, the FPGA implementation demonstrates the feasibility of deploying such advanced models where immediate decision-making is crucial for crop protection. This analysis paves the way for sustainable olive cultivation practices, ensuring the longevity and resilience of this invaluable crop in the face of evolving disease challenges. © 2023 IEEE.

关键词： Diseases

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Efficient Categorization of Pneumonia Diagnosis Using Low-Power Embedded Devices 8

Efficient Categorization of Pneumonia Diagnosis Using Low-Po...

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8th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2023

作者： Sanida, Theodora Sanida, Maria Vasiliki Sideris, Argyrios Dossis, Michael Dasygenis, Minas University of Western Macedonia Department of Electrical & Computer Engineering Kozani50131 Greece University of Piraeus Department of Digital Systems Piraeus18534 Greece

ISBN: (纸本)9798350383676

The lungs are integral to facilitating the respiratory processes crucial for human survival. However, lung infections can pose severe threats to human health. In medical diagnostics, numerous tools and techniques exist for assessing and diagnosing lung-related issues. Among these, Chest X-ray images have emerged as a widely favoured choice due to their distinct advantages, such as accessibility and the ability to provide valuable insights into pulmonary conditions. This work is dedicated to developing a robust multiclass category system for Chest X-ray images. A notable feature of this work is its emphasis on the efficient execution of the categorization work on a low-power heterogeneous embedded device, demonstrating the potential for practical applications in resource-constrained environments. One of this study's key highlights lies in exploring different optimization algorithms and their impact on category accuracy. The study conducts comprehensive experiments utilizing four prominent optimizers: Adam, Adamax, RMSprop, and SGD. Through these experiments, we observed that the proposed modified design attained the highest rate of accuracy, 97.16% when the Adam optimizer was employed. This outcome underscores the significance of optimizer selection in developing accurate and reliable diagnostic models for Chest X-ray image categorization, ultimately contributing to advancements in medical imaging and healthcare. © 2023 IEEE.

关键词： Medical imaging

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Accelerating CNNs for Pneumonia Disease Diagnosis via Heterogeneous FPGA systems 9

Accelerating CNNs for Pneumonia Disease Diagnosis via Hetero...

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9th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2024

作者： Sanida, Theodora Sideris, Argyrios Sanida, Maria Vasiliki Dossis, Michael Dasygenis, Minas University of Western Macedonia Department of Electrical and Computer Engineering Kozani50131 Greece University of Piraeus Department of Digital Systems Piraeus18534 Greece

ISBN: (纸本)9798350342482

Early and accurate diagnosis of pneumonia is critical for effective treatment and improved patient outcomes. This paper introduces a cutting-edge framework for accelerating the diagnosis of pneumonia using Convolutional Neural Networks (CNNs) on heterogeneous Field-Programmable Gate Array (FPGA) systems. The framework is designed to leverage FPGAs' parallel processing capabilities and reconfigurability, enabling significant enhancements in speed and efficiency for processing medical images. Our approach involves the development of an optimized CNN model tailored to the features and characteristics typical of pneumonia in radiographic images. This model is then mapped onto an FPGA, utilizing high-level synthesis and custom hardware descriptions to maximize data throughput and minimize latency. We conducted testing using a diverse chest X-ray dataset, comparing our FPGA-based system's performance against traditional computing platforms such as GPUs and CPUs. Results indicate that our system accelerates the diagnostic process over 5 times and achieves a 97.58% accuracy rate comparable to state-of-the-art methods. The proposed framework enhances medical imaging devices' capabilities, leading to quicker diagnoses, thus reducing hospitalization time and improving patient care. © 2024 IEEE.

关键词： digital storage

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Key Reduction of McEliece's Cryptosystem Using List Decoding

Key Reduction of McEliece's Cryptosystem Using List Decoding

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IEEE International Symposium on Information Theory (ISIT)

作者： Barbier, Morgan Barreto, Paulo S. L. M. Computer Science Laboratory of École Polytechnique LIX INRIA Saclay Île France Department of Computer and Digital Systems Engineering Escola Politécnica University of São Paulo Brazil

ISBN: (纸本)9781457705953

Different variants of the code-based McEliece cryptosystem were proposed to reduce the size of the public key. All these variants use very structured codes, which open the door to new attacks exploiting the underlying structure. In this paper, we show that the quasi-dyadic variant can be designed to resist all known attacks. In light of a new study on list decoding algorithms for binary Goppa codes, we explain, as already suggested by Bernstein, Lange and Peters, how to increase the security level for given public keysizes. Using the state-of-the-art list decoding algorithm instead of unique decoding, we exhibit a keysize gain of about 4% for the standard McEliece cryptosystem and up to 21% for the adjusted quasi-dyadic variant.

关键词： computer science Decoding Goppa codes Polynomials Public key binary Goppa codes binary codes code-based McEliece cryptosystem decoding decoding algorithms public key cryptography public key size rduction quasidyadic variant security level state-of-the-art list decoding algorithm

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Accelerate Processing of Image with the Keccak-512 Algorithm on Cryptoprocessor 8

Accelerate Processing of Image with the Keccak-512 Algorithm...

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8th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2023

作者： Sideris, Argyrios Sanida, Theodora Sanida, Maria Vasiliki Dossis, Michael Dasygenis, Minas University of Western Macedonia Department of Electrical & Computer Engineering Kozani50131 Greece University of Piraeus Department of Digital Systems Piraeus18534 Greece

ISBN: (纸本)9798350383676

Image hashing is a crucial technique for content authentication and image retrieval in various applications, including multimedia databases and copyright protection. This study introduces a novel approach to image hashing that leverages the capabilities of FPGAs and harnesses the cryptographic strength and computational efficiency of the Keccak-512 algorithm. Our experimentations were conducted on the Intel Arria 10 GX FPGA board, and the versatile Nios II enhanced micropro-cessor. The results gleaned from these experiments underscore the tremendous potential of our FPGA-based image-hashing approach. It presents an alluring solution, merging efficiency with security, effectively addressing the burgeoning demands for content authentication and image integrity verification across diverse image-centric applications. This approach provides a powerful tool for addressing the increasing demand for image authentication and integrity verification in the digital age. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Acceleration of GANs for Potato Crop Disease Identification via FPGA 8

Acceleration of GANs for Potato Crop Disease Identification ...

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8th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2023

ISBN: (纸本)9798350383676

Effective management of plant diseases is essential for ensuring food protection, as a wide range of diseases can significantly reduce crop yields. Potatoes, a staple crop worldwide, are particularly susceptible to various diseases. The timely identification and early warning systems are pivotal in curbing the proliferation of potato diseases and ultimately increasing crop productivity. In recent times, Generative Adversarial Networks (GANs) have emerged as revolutionary technology within the realm of artificial intelligence and machine learning. These networks provide a diverse dataset of synthetic images, thereby facilitating the development of robust computer vision algorithms capable of accurately discerning and categorizing various potato crop diseases. In the scope of our work, we have implemented and accelerated GAN on a Xilinx FPGA SoC. This platform has exceptional efficiency in handling such challenges in terms of power consumption and performance. Our method has also achieved superior power and performance efficiency compared to conventional GPU and CPU setups, boasting an impressive 0.015 ms average time per image in the FPGA's configuration. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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