The availability of many-core computing platforms enables a wide variety of technical solutions for systems across the embedded, high-performance and cloud computing domains. However, large scale manycore systems are notoriously hard to optimise. Choices regarding resource allocation alone can account for wide variability in timeliness and energy dissipation (up to several orders of magnitude). Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing covers dynamic resource allocation heuristics for manycore systems, aiming to provide appropriate guarantees on performance and energy efficiency. It addresses different types of systems, aiming to harmonise the approaches to dynamic allocation across the complete spectrum between systems with little flexibility and strict real-time guarantees all the way to highly dynamic systems with soft performance requirements. Technical topics presented in the book include: • Load and Resource Models• Admission Control• Feedback-based Allocation and Optimisation• Search-based Allocation Heuristics• Distributed Allocation based on Swarm Intelligence• Value-Based AllocationEach of the topics is illustrated with examples based on realistic computational platforms such as Network-on-Chip manycore processors, grids and private cloud environments.

The design of today's semiconductor chips for various applications, such as telecommunications, poses various challenges due to the complexity of these systems. These highly complex systems-on-chips demand new approaches to connect and manage the communication between on-chip processing and storage components and networks on chips (NoCs) provide a powerful solution. This book is the first to provide a unified overview of NoC technology. It includes in-depth analysis of all the on-chip communication challenges, from physical wiring implementation up to software architecture, and a complete classification of their various Network-on-Chip approaches and solutions.* Leading-edge research from world-renowned experts in academia and industry with state-of-the-art technology implementations/trends* An integrated presentation not currently available in any other book* A thorough introduction to current design methodologies and chips designed with NoCs

Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary. Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures Work with Arduino and ultra-low-power microcontrollers Learn the essentials of ML and how to train your own models Train models to understand audio, image, and accelerometer data Explore TensorFlow Lite for Microcontrollers, Google’s toolkit for TinyML Debug applications and provide safeguards for privacy and security Optimize latency, energy usage, and model and binary size

As the number of processor cores and IP blocks integrated on a single chip is steadily growing, a systematic approach to design the communication infrastructure becomes necessary. Different variants of packed switched on-chip networks have been proposed by several groups during the past two years. This book summarizes the state of the art of these efforts and discusses the major issues from the physical integration to architecture to operating systems and application interfaces. It also provides a guideline and vision about the direction this field is moving to. Moreover, the book outlines the consequences of adopting design platforms based on packet switched network. The consequences may in fact be far reaching because many of the topics of distributed systems, distributed real-time systems, fault tolerant systems, parallel computer architecture, parallel programming as well as traditional system-on-chip issues will appear relevant but within the constraints of a single chip VLSI implementation.

Heterogeneous Computing with OpenCL, Second Edition teaches OpenCL and parallel programming for complex systems that may include a variety of device architectures: multi-core CPUs, GPUs, and fully-integrated Accelerated Processing Units (APUs) such as AMD Fusion technology. It is the first textbook that presents OpenCL programming appropriate for the classroom and is intended to support a parallel programming course. Students will come away from this text with hands-on experience and significant knowledge of the syntax and use of OpenCL to address a range of fundamental parallel algorithms. Designed to work on multiple platforms and with wide industry support, OpenCL will help you more effectively program for a heterogeneous future. Written by leaders in the parallel computing and OpenCL communities, Heterogeneous Computing with OpenCL explores memory spaces, optimization techniques, graphics interoperability, extensions, and debugging and profiling. It includes detailed examples throughout, plus additional online exercises and other supporting materials that can be downloaded at http://www.heterogeneouscompute.org/?page_id=7 This book will appeal to software engineers, programmers, hardware engineers, and students/advanced students. Explains principles and strategies to learn parallel programming with OpenCL, from understanding the four abstraction models to thoroughly testing and debugging complete applications. Covers image processing, web plugins, particle simulations, video editing, performance optimization, and more. Shows how OpenCL maps to an example target architecture and explains some of the tradeoffs associated with mapping to various architectures Addresses a range of fundamental programming techniques, with multiple examples and case studies that demonstrate OpenCL extensions for a variety of hardware platforms

More useful techniques, tips, and tricks for harnessing the power of the new generation of powerful GPUs.