Author: Alex Orailoglu
Publisher: Springer Nature
ISBN: 3030609391
Category : Computers
Languages : en
Pages : 372
Book Description
This book constitutes the refereed proceedings of the 20th International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation, SAMOS 2020, held in Samos, Greece, in July 2020.* The 16 regular papers presented were carefully reviewed and selected from 35 submissions. In addition, 9 papers from two special sessions were included, which were organized on topics of current interest: innovative architectures for security and European projects on embedded and high performance computing for health applications. * The conference was held virtually due to the COVID-19 pandemic.
Embedded Computer Systems: Architectures, Modeling, and Simulation
Author: Alex Orailoglu
Publisher: Springer Nature
ISBN: 3030609391
Category : Computers
Languages : en
Pages : 372
Book Description
This book constitutes the refereed proceedings of the 20th International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation, SAMOS 2020, held in Samos, Greece, in July 2020.* The 16 regular papers presented were carefully reviewed and selected from 35 submissions. In addition, 9 papers from two special sessions were included, which were organized on topics of current interest: innovative architectures for security and European projects on embedded and high performance computing for health applications. * The conference was held virtually due to the COVID-19 pandemic.
Publisher: Springer Nature
ISBN: 3030609391
Category : Computers
Languages : en
Pages : 372
Book Description
This book constitutes the refereed proceedings of the 20th International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation, SAMOS 2020, held in Samos, Greece, in July 2020.* The 16 regular papers presented were carefully reviewed and selected from 35 submissions. In addition, 9 papers from two special sessions were included, which were organized on topics of current interest: innovative architectures for security and European projects on embedded and high performance computing for health applications. * The conference was held virtually due to the COVID-19 pandemic.
Guide to DataFlow Supercomputing
Author: Veljko Milutinović
Publisher: Springer
ISBN: 3319162292
Category : Computers
Languages : en
Pages : 136
Book Description
This unique text/reference describes an exciting and novel approach to supercomputing in the DataFlow paradigm. The major advantages and applications of this approach are clearly described, and a detailed explanation of the programming model is provided using simple yet effective examples. The work is developed from a series of lecture courses taught by the authors in more than 40 universities across more than 20 countries, and from research carried out by Maxeler Technologies, Inc. Topics and features: presents a thorough introduction to DataFlow supercomputing for big data problems; reviews the latest research on the DataFlow architecture and its applications; introduces a new method for the rapid handling of real-world challenges involving large datasets; provides a case study on the use of the new approach to accelerate the Cooley-Tukey algorithm on a DataFlow machine; includes a step-by-step guide to the web-based integrated development environment WebIDE.
Publisher: Springer
ISBN: 3319162292
Category : Computers
Languages : en
Pages : 136
Book Description
This unique text/reference describes an exciting and novel approach to supercomputing in the DataFlow paradigm. The major advantages and applications of this approach are clearly described, and a detailed explanation of the programming model is provided using simple yet effective examples. The work is developed from a series of lecture courses taught by the authors in more than 40 universities across more than 20 countries, and from research carried out by Maxeler Technologies, Inc. Topics and features: presents a thorough introduction to DataFlow supercomputing for big data problems; reviews the latest research on the DataFlow architecture and its applications; introduces a new method for the rapid handling of real-world challenges involving large datasets; provides a case study on the use of the new approach to accelerate the Cooley-Tukey algorithm on a DataFlow machine; includes a step-by-step guide to the web-based integrated development environment WebIDE.
Towards Heterogeneous Multi-core Systems-on-Chip for Edge Machine Learning
Author: Vikram Jain
Publisher: Springer Nature
ISBN: 3031382307
Category : Technology & Engineering
Languages : en
Pages : 199
Book Description
This book explores and motivates the need for building homogeneous and heterogeneous multi-core systems for machine learning to enable flexibility and energy-efficiency. Coverage focuses on a key aspect of the challenges of (extreme-)edge-computing, i.e., design of energy-efficient and flexible hardware architectures, and hardware-software co-optimization strategies to enable early design space exploration of hardware architectures. The authors investigate possible design solutions for building single-core specialized hardware accelerators for machine learning and motivates the need for building homogeneous and heterogeneous multi-core systems to enable flexibility and energy-efficiency. The advantages of scaling to heterogeneous multi-core systems are shown through the implementation of multiple test chips and architectural optimizations.
Publisher: Springer Nature
ISBN: 3031382307
Category : Technology & Engineering
Languages : en
Pages : 199
Book Description
This book explores and motivates the need for building homogeneous and heterogeneous multi-core systems for machine learning to enable flexibility and energy-efficiency. Coverage focuses on a key aspect of the challenges of (extreme-)edge-computing, i.e., design of energy-efficient and flexible hardware architectures, and hardware-software co-optimization strategies to enable early design space exploration of hardware architectures. The authors investigate possible design solutions for building single-core specialized hardware accelerators for machine learning and motivates the need for building homogeneous and heterogeneous multi-core systems to enable flexibility and energy-efficiency. The advantages of scaling to heterogeneous multi-core systems are shown through the implementation of multiple test chips and architectural optimizations.
Efficient Processing of Deep Neural Networks
Author: Vivienne Sze
Publisher: Springer Nature
ISBN: 3031017668
Category : Technology & Engineering
Languages : en
Pages : 254
Book Description
This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems. The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary work that provide insights that may spark new ideas.
Publisher: Springer Nature
ISBN: 3031017668
Category : Technology & Engineering
Languages : en
Pages : 254
Book Description
This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems. The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary work that provide insights that may spark new ideas.
Heterogeneous Computing Architectures
Author: Olivier Terzo
Publisher: CRC Press
ISBN: 0429680031
Category : Computers
Languages : en
Pages : 287
Book Description
Heterogeneous Computing Architectures: Challenges and Vision provides an updated vision of the state-of-the-art of heterogeneous computing systems, covering all the aspects related to their design: from the architecture and programming models to hardware/software integration and orchestration to real-time and security requirements. The transitions from multicore processors, GPU computing, and Cloud computing are not separate trends, but aspects of a single trend-mainstream; computers from desktop to smartphones are being permanently transformed into heterogeneous supercomputer clusters. The reader will get an organic perspective of modern heterogeneous systems and their future evolution.
Publisher: CRC Press
ISBN: 0429680031
Category : Computers
Languages : en
Pages : 287
Book Description
Heterogeneous Computing Architectures: Challenges and Vision provides an updated vision of the state-of-the-art of heterogeneous computing systems, covering all the aspects related to their design: from the architecture and programming models to hardware/software integration and orchestration to real-time and security requirements. The transitions from multicore processors, GPU computing, and Cloud computing are not separate trends, but aspects of a single trend-mainstream; computers from desktop to smartphones are being permanently transformed into heterogeneous supercomputer clusters. The reader will get an organic perspective of modern heterogeneous systems and their future evolution.
Embedded Computer Systems: Architectures, Modeling, and Simulation
Author: Dionisios N. Pnevmatikatos
Publisher: Springer
ISBN: 3030275620
Category : Computers
Languages : en
Pages : 486
Book Description
This book constitutes the refereed proceedings of the 19th International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation, SAMOS 2019, held in Pythagorion, Samos, Greece, in July 2019. The 21 regular papers presented were carefully reviewed and selected from 55 submissions. The papers are organized in topical sections on system design space exploration; deep learning optimization; system security; multi/many-core scheduling; system energy and heat management; many-core communication; and electronic system-level design and verification. In addition there are 13 papers from three special sessions which were organized on topics of current interest: insights from negative results; machine learning implementations; and European projects.
Publisher: Springer
ISBN: 3030275620
Category : Computers
Languages : en
Pages : 486
Book Description
This book constitutes the refereed proceedings of the 19th International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation, SAMOS 2019, held in Pythagorion, Samos, Greece, in July 2019. The 21 regular papers presented were carefully reviewed and selected from 55 submissions. The papers are organized in topical sections on system design space exploration; deep learning optimization; system security; multi/many-core scheduling; system energy and heat management; many-core communication; and electronic system-level design and verification. In addition there are 13 papers from three special sessions which were organized on topics of current interest: insights from negative results; machine learning implementations; and European projects.
High Performance Computing
Author: Juan Luis Crespo-Mariño
Publisher: Springer Nature
ISBN: 3030410056
Category : Computers
Languages : en
Pages : 488
Book Description
This book constitutes the refereed proceedings of the 6th Latin American High Performance Computing Conference, CARLA 2019, held in Turrialba, Costa Rica, in September 2019. The 32 revised full papers presented were carefully reviewed and selected out of 62 submissions. The papers included in this book are organized according to the conference tracks - regular track on high performance computing: applications; algorithms and models; architectures and infrastructures; and special track on bioinspired processing (BIP): neural and evolutionary approaches; image and signal processing; biodiversity informatics and computational biology.
Publisher: Springer Nature
ISBN: 3030410056
Category : Computers
Languages : en
Pages : 488
Book Description
This book constitutes the refereed proceedings of the 6th Latin American High Performance Computing Conference, CARLA 2019, held in Turrialba, Costa Rica, in September 2019. The 32 revised full papers presented were carefully reviewed and selected out of 62 submissions. The papers included in this book are organized according to the conference tracks - regular track on high performance computing: applications; algorithms and models; architectures and infrastructures; and special track on bioinspired processing (BIP): neural and evolutionary approaches; image and signal processing; biodiversity informatics and computational biology.
Formal Modeling and Verification of Cyber-Physical Systems
Author: Rolf Drechsler
Publisher: Springer
ISBN: 3658099941
Category : Computers
Languages : en
Pages : 324
Book Description
This book presents the lecture notes of the 1st Summer School on Methods and Tools for the Design of Digital Systems, 2015, held in Bremen, Germany. The topic of the summer school was devoted to modeling and verification of cyber-physical systems. This covers several aspects of the field, including hybrid systems and model checking, as well as applications in robotics and aerospace systems. The main chapters have been written by leading scientists, who present their field of research, each providing references to introductory material as well as latest scientific advances and future research directions. This is complemented by short papers submitted by the participating PhD students.
Publisher: Springer
ISBN: 3658099941
Category : Computers
Languages : en
Pages : 324
Book Description
This book presents the lecture notes of the 1st Summer School on Methods and Tools for the Design of Digital Systems, 2015, held in Bremen, Germany. The topic of the summer school was devoted to modeling and verification of cyber-physical systems. This covers several aspects of the field, including hybrid systems and model checking, as well as applications in robotics and aerospace systems. The main chapters have been written by leading scientists, who present their field of research, each providing references to introductory material as well as latest scientific advances and future research directions. This is complemented by short papers submitted by the participating PhD students.
Heterogenous Computational Intelligence in Internet of Things
Author: Pawan Singh
Publisher: CRC Press
ISBN: 1000967948
Category : Computers
Languages : en
Pages : 376
Book Description
We have seen a sharp increase in the development of data transfer techniques in the networking industry over the past few years. We can see that the photos are assisting clinicians in detecting infection in patients even in the current COVID-19 pandemic condition. With the aid of ML/AI, medical imaging, such as lung X-rays for COVID-19 infection, is crucial in the early detection of many diseases. We also learned that in the COVID-19 scenario, both wired and wireless networking are improved for data transfer but have network congestion. An intriguing concept that has the ability to reduce spectrum congestion and continuously offer new network services is providing wireless network virtualization. The degree of virtualization and resource sharing varies between the paradigms. Each paradigm has both technical and non-technical issues that need to be handled before wireless virtualization becomes a common technology. For wireless network virtualization to be successful, these issues need careful design and evaluation. Future wireless network architecture must adhere to a number of Quality of Service (QoS) requirements. Virtualization has been extended to wireless networks as well as conventional ones. By enabling multi-tenancy and tailored services with a wider range of carrier frequencies, it improves efficiency and utilization. In the IoT environment, wireless users are heterogeneous, and the network state is dynamic, making network control problems extremely difficult to solve as dimensionality and computational complexity keep rising quickly. Deep Reinforcement Learning (DRL) has been developed by the use of Deep Neural Networks (DNNs) as a potential approach to solve high-dimensional and continuous control issues effectively. Deep Reinforcement Learning techniques provide great potential in IoT, edge and SDN scenarios and are used in heterogeneous networks for IoT-based management on the QoS required by each Software Defined Network (SDN) service. While DRL has shown great potential to solve emerging problems in complex wireless network virtualization, there are still domain-specific challenges that require further study, including the design of adequate DNN architectures with 5G network optimization issues, resource discovery and allocation, developing intelligent mechanisms that allow the automated and dynamic management of the virtual communications established in the SDNs which is considered as research perspective.
Publisher: CRC Press
ISBN: 1000967948
Category : Computers
Languages : en
Pages : 376
Book Description
We have seen a sharp increase in the development of data transfer techniques in the networking industry over the past few years. We can see that the photos are assisting clinicians in detecting infection in patients even in the current COVID-19 pandemic condition. With the aid of ML/AI, medical imaging, such as lung X-rays for COVID-19 infection, is crucial in the early detection of many diseases. We also learned that in the COVID-19 scenario, both wired and wireless networking are improved for data transfer but have network congestion. An intriguing concept that has the ability to reduce spectrum congestion and continuously offer new network services is providing wireless network virtualization. The degree of virtualization and resource sharing varies between the paradigms. Each paradigm has both technical and non-technical issues that need to be handled before wireless virtualization becomes a common technology. For wireless network virtualization to be successful, these issues need careful design and evaluation. Future wireless network architecture must adhere to a number of Quality of Service (QoS) requirements. Virtualization has been extended to wireless networks as well as conventional ones. By enabling multi-tenancy and tailored services with a wider range of carrier frequencies, it improves efficiency and utilization. In the IoT environment, wireless users are heterogeneous, and the network state is dynamic, making network control problems extremely difficult to solve as dimensionality and computational complexity keep rising quickly. Deep Reinforcement Learning (DRL) has been developed by the use of Deep Neural Networks (DNNs) as a potential approach to solve high-dimensional and continuous control issues effectively. Deep Reinforcement Learning techniques provide great potential in IoT, edge and SDN scenarios and are used in heterogeneous networks for IoT-based management on the QoS required by each Software Defined Network (SDN) service. While DRL has shown great potential to solve emerging problems in complex wireless network virtualization, there are still domain-specific challenges that require further study, including the design of adequate DNN architectures with 5G network optimization issues, resource discovery and allocation, developing intelligent mechanisms that allow the automated and dynamic management of the virtual communications established in the SDNs which is considered as research perspective.
High-Performance Modelling and Simulation for Big Data Applications
Author: Joanna Kołodziej
Publisher: Springer
ISBN: 3030162729
Category : Computers
Languages : en
Pages : 364
Book Description
This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications.
Publisher: Springer
ISBN: 3030162729
Category : Computers
Languages : en
Pages : 364
Book Description
This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications.