Download or read book Embedded Computer Systems Architectures Modeling and Simulation written by Alex Orailoglu and published by Springer Nature. This book was released on 2020-10-14 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Guide to DataFlow Supercomputing written by Veljko Milutinović and published by Springer. This book was released on 2015-04-28 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Towards Heterogeneous Multi core Systems on Chip for Edge Machine Learning written by Vikram Jain and published by Springer Nature. This book was released on 2023-09-15 with total page 199 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Efficient Processing of Deep Neural Networks written by Vivienne Sze and published by Springer Nature. This book was released on 2022-05-31 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Heterogeneous Computing Architectures written by Olivier Terzo and published by CRC Press. This book was released on 2019-09-10 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Embedded Computer Systems Architectures Modeling and Simulation written by Dionisios N. Pnevmatikatos and published by Springer. This book was released on 2019-08-09 with total page 486 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book High Performance Computing written by Juan Luis Crespo-Mariño and published by Springer Nature. This book was released on 2020-02-12 with total page 488 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Formal Modeling and Verification of Cyber Physical Systems written by Rolf Drechsler and published by Springer. This book was released on 2015-06-05 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Heterogenous Computational Intelligence in Internet of Things written by Pawan Singh and published by CRC Press. This book was released on 2023-10-23 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book High Performance Modelling and Simulation for Big Data Applications written by Joanna Kołodziej and published by Springer. This book was released on 2019-03-25 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Driving Scientific and Engineering Discoveries Through the Convergence of HPC Big Data and AI written by Jeffrey Nichols and published by Springer Nature. This book was released on 2020-12-22 with total page 555 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the revised selected papers of the 17th Smoky Mountains Computational Sciences and Engineering Conference, SMC 2020, held in Oak Ridge, TN, USA*, in August 2020. The 36 full papers and 1 short paper presented were carefully reviewed and selected from a total of 94 submissions. The papers are organized in topical sections of computational applications: converged HPC and artificial intelligence; system software: data infrastructure and life cycle; experimental/observational applications: use cases that drive requirements for AI and HPC convergence; deploying computation: on the road to a converged ecosystem; scientific data challenges. *The conference was held virtually due to the COVID-19 pandemic.

Download or read book Low Power Circuit Design Using Advanced CMOS Technology written by Milin Zhang and published by CRC Press. This book was released on 2022-09-01 with total page 776 pages. Available in PDF, EPUB and Kindle. Book excerpt: Low Power Circuit Design Using Advanced CMOS Technology is a summary of lectures from the first Advanced CMOS Technology Summer School (ACTS) 2017. The slides are selected from the handouts, while the text was edited according to the lecturers talk.ACTS is a joint activity supported by the IEEE Circuit and System Society (CASS) and the IEEE Solid-State Circuits Society (SSCS). The goal of the school is to provide society members as well researchers and engineers from industry the opportunity to learn about new emerging areas from leading experts in the field. ACTS is an example of high-level continuous education for junior engineers, teachers in academe, and students. ACTS was the results of a successful collaboration between societies, the local chapter leaders, and industry leaders. This summer school was the brainchild of Dr. Zhihua Wang, with strong support from volunteers from both the IEEE SSCS and CASS. In addition, the local companies, Synopsys China and Beijing IC Park, provided support.This first ACTS was held in the summer 2017 in Beijing. The lectures were given by academic researchers and industry experts, who presented each 6-hour long lectures on topics covering process technology, EDA skill, and circuit and layout design skills. The school was hosted and organized by the CASS Beijing Chapter, SSCS Beijing Chapter, and SSCS Tsinghua Student Chapter. The co-chairs of the first ACTS were Dr. Milin Zhang, Dr. Hanjun Jiang and Dr. Liyuan Liu. The first ACTS was a great success as illustrated by the many participants from all over China as well as by the publicity it has been received in various media outlets, including Xinhua News, one of the most popular news channels in China.

Download or read book Hardware for Artificial Intelligence written by Alexantrou Serb and published by Frontiers Media SA. This book was released on 2022-09-26 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book High level Synthesis written by Michael Fingeroff and published by Xlibris Corporation. This book was released on 2010 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: Are you an RTL or system designer that is currently using, moving, or planning to move to an HLS design environment? Finally, a comprehensive guide for designing hardware using C++ is here. Michael Fingeroff's High-Level Synthesis Blue Book presents the most effective C++ synthesis coding style for achieving high quality RTL. Master a totally new design methodology for coding increasingly complex designs! This book provides a step-by-step approach to using C++ as a hardware design language, including an introduction to the basics of HLS using concepts familiar to RTL designers. Each chapter provides easy-to-understand C++ examples, along with hardware and timing diagrams where appropriate. The book progresses from simple concepts such as sequential logic design to more complicated topics such as memory architecture and hierarchical sub-system design. Later chapters bring together many of the earlier HLS design concepts through their application in simplified design examples. These examples illustrate the fundamental principles behind C++ hardware design, which will translate to much larger designs. Although this book focuses primarily on C and C++ to present the basics of C++ synthesis, all of the concepts are equally applicable to SystemC when describing the core algorithmic part of a design. On completion of this book, readers should be well on their way to becoming experts in high-level synthesis.

Download or read book Deep Learning for Computer Architects written by Brandon Reagen and published by Springer Nature. This book was released on 2022-05-31 with total page 109 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning, and specifically deep learning, has been hugely disruptive in many fields of computer science. The success of deep learning techniques in solving notoriously difficult classification and regression problems has resulted in their rapid adoption in solving real-world problems. The emergence of deep learning is widely attributed to a virtuous cycle whereby fundamental advancements in training deeper models were enabled by the availability of massive datasets and high-performance computer hardware. This text serves as a primer for computer architects in a new and rapidly evolving field. We review how machine learning has evolved since its inception in the 1960s and track the key developments leading up to the emergence of the powerful deep learning techniques that emerged in the last decade. Next we review representative workloads, including the most commonly used datasets and seminal networks across a variety of domains. In addition to discussing the workloads themselves, we also detail the most popular deep learning tools and show how aspiring practitioners can use the tools with the workloads to characterize and optimize DNNs. The remainder of the book is dedicated to the design and optimization of hardware and architectures for machine learning. As high-performance hardware was so instrumental in the success of machine learning becoming a practical solution, this chapter recounts a variety of optimizations proposed recently to further improve future designs. Finally, we present a review of recent research published in the area as well as a taxonomy to help readers understand how various contributions fall in context.

Download or read book Machine Learning in VLSI Computer Aided Design written by Ibrahim (Abe) M. Elfadel and published by Springer. This book was released on 2019-03-15 with total page 697 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides readers with an up-to-date account of the use of machine learning frameworks, methodologies, algorithms and techniques in the context of computer-aided design (CAD) for very-large-scale integrated circuits (VLSI). Coverage includes the various machine learning methods used in lithography, physical design, yield prediction, post-silicon performance analysis, reliability and failure analysis, power and thermal analysis, analog design, logic synthesis, verification, and neuromorphic design. Provides up-to-date information on machine learning in VLSI CAD for device modeling, layout verifications, yield prediction, post-silicon validation, and reliability; Discusses the use of machine learning techniques in the context of analog and digital synthesis; Demonstrates how to formulate VLSI CAD objectives as machine learning problems and provides a comprehensive treatment of their efficient solutions; Discusses the tradeoff between the cost of collecting data and prediction accuracy and provides a methodology for using prior data to reduce cost of data collection in the design, testing and validation of both analog and digital VLSI designs. From the Foreword As the semiconductor industry embraces the rising swell of cognitive systems and edge intelligence, this book could serve as a harbinger and example of the osmosis that will exist between our cognitive structures and methods, on the one hand, and the hardware architectures and technologies that will support them, on the other....As we transition from the computing era to the cognitive one, it behooves us to remember the success story of VLSI CAD and to earnestly seek the help of the invisible hand so that our future cognitive systems are used to design more powerful cognitive systems. This book is very much aligned with this on-going transition from computing to cognition, and it is with deep pleasure that I recommend it to all those who are actively engaged in this exciting transformation. Dr. Ruchir Puri, IBM Fellow, IBM Watson CTO & Chief Architect, IBM T. J. Watson Research Center

Download or read book Deep Learning for Numerical Applications with SAS Hardcover Edition written by Henry Bequet and published by . This book was released on 2019-08-16 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: Foreword by Oliver Schabenberger, PhD Executive Vice President, Chief Operating Officer and Chief Technology Officer SAS Dive into deep learning! Machine learning and deep learning are ubiquitous in our homes and workplaces-from machine translation to image recognition and predictive analytics to autonomous driving. Deep learning holds the promise of improving many everyday tasks in a variety of disciplines. Much deep learning literature explains the mechanics of deep learning with the goal of implementing cognitive applications fueled by Big Data. This book is different. Written by an expert in high-performance analytics, Deep Learning for Numerical Applications with SAS introduces a new field: Deep Learning for Numerical Applications (DL4NA). Contrary to deep learning, the primary goal of DL4NA is not to learn from data but to dramatically improve the performance of numerical applications by training deep neural networks. Deep Learning for Numerical Applications with SAS presents deep learning concepts in SAS along with step-by-step techniques that allow you to easily reproduce the examples on your high-performance analytics systems. It also discusses the latest hardware innovations that can power your SAS programs: from many-core CPUs to GPUs to FPGAs to ASICs. This book assumes the reader has no prior knowledge of high-performance computing, machine learning, or deep learning. It is intended for SAS developers who want to develop and run the fastest analytics. In addition to discovering the latest trends in hybrid architectures with GPUs and FPGAS, readers will learn how to Use deep learning in SAS Speed up their analytics using deep learning Easily write highly parallel programs using the many task computing paradigms