Improving Emerging Systems' Efficiency with Hardware Accelerators - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Improving Emerging Systems' Efficiency with Hardware Accelerators write by Henrique Fingler. This book was released on 2023. Improving Emerging Systems' Efficiency with Hardware Accelerators available in PDF, EPUB and Kindle. The constant growth of datacenters and cloud computing comes with an increase of power consumption. With the end of Dennard scaling and Moore's law, computing no longer grows at the same ratio as transistor count and density grows. This thesis explores ideas to increase computing efficiency, which is defined as the ratio of processing power per energy spent. Hardware acceleration is an established technique to improve computing efficiency by specializing hardware to a subset of operations or application domains. While accelerators have fueled the success of some application domains such as machine learning, accelerator programming interfaces and runtimes have significant limitations that collectively form barriers to adoption in many settings. There are great opportunities for extending hardware acceleration interfaces to more application domains and other platforms. First, this thesis presents DGSF, a framework that enables serverless platforms to access disaggregated accelerators (GPUs). DGSF uses virtualization techniques to provide serverless platforms with GPUs, with the abstraction of a local GPU that can be backed by a local or a remote physical GPU. Through optimizations specific to serverless platforms, applications that use a GPU can have a lower end-to-end execution time than if they were run natively, using a local physical GPU. DGSF extends hardware acceleration accessibility to an existing serverless platforms which currently does not support accelerators, showing the flexibility and ease of deployment of the DGSF framework. Next, this thesis presents LAKE, a framework that introduces accelerator and machine learning support to operating system kernels. I believe there is great potential to replace operating system resource management heuristics with machine learning, for example, I/O and process scheduling. Accelerators are vital to support efficient, low latency inference for kernels that makes frequent use of ML techniques. Unfortunately, operating systems can not access hardware acceleration. LAKE uses GPU virtualization techniques to efficiently enable accelerator accessibility in operating systems. However, allowing operating systems to use hardware acceleration introduces problems unique to this scenario. User and kernel applications can contend for resources such as CPU or accelerators. Unmanaged resource contention can harm the performance of applications. Machine learning-based kernel subsystems can produce unsatisfactory results. There need to be guardrails, mechanisms that prevent machine learning models to output solutions with quality below a threshold, to avoid poor decisions and performance pathologies. LAKE proposes customizable, developer written policies that can control contention, modulate execution and provide guardrails to machine learning. Finally, this thesis proposes LFR, a feature registry that augments LAKE to provide a shared feature and model registry framework to support future ML-in-the-kernel applications, removing the need of ad hoc designs. The learnings from LAKE showed that machine learning in operating systems can increase computing efficiency and revealed the absence of a shared framework. Such framework is a required component in future research and production of machine learning driven operating systems. LFR introduces an in-kernel feature registry that provides machine learning-based kernel subsystems with a common API to store, capture and manage models and feature vectors, and facilitates the insertion of inference hooks into the kernel. This thesis studies the application of LFR, and evaluates the performance critical parts, such as capturing and storing features
Hardware Accelerator Systems for Artificial Intelligence and Machine Learning
Hardware Accelerator Systems for Artificial Intelligence and Machine Learning - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Hardware Accelerator Systems for Artificial Intelligence and Machine Learning write by . This book was released on 2021-03-28. Hardware Accelerator Systems for Artificial Intelligence and Machine Learning available in PDF, EPUB and Kindle. Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Volume 122 delves into arti?cial Intelligence and the growth it has seen with the advent of Deep Neural Networks (DNNs) and Machine Learning. Updates in this release include chapters on Hardware accelerator systems for artificial intelligence and machine learning, Introduction to Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Deep Learning with GPUs, Edge Computing Optimization of Deep Learning Models for Specialized Tensor Processing Architectures, Architecture of NPU for DNN, Hardware Architecture for Convolutional Neural Network for Image Processing, FPGA based Neural Network Accelerators, and much more. Updates on new information on the architecture of GPU, NPU and DNN Discusses In-memory computing, Machine intelligence and Quantum computing Includes sections on Hardware Accelerator Systems to improve processing efficiency and performance
Energy Efficient Embedded Video Processing Systems
Energy Efficient Embedded Video Processing Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Energy Efficient Embedded Video Processing Systems write by Muhammad Usman Karim Khan. This book was released on 2017-09-17. Energy Efficient Embedded Video Processing Systems available in PDF, EPUB and Kindle. This book provides its readers with the means to implement energy-efficient video systems, by using different optimization approaches at multiple abstraction levels. The authors evaluate the complete video system with a motive to optimize its different software and hardware components in synergy, increase the throughput-per-watt, and address reliability issues. Subsequently, this book provides algorithmic and architectural enhancements, best practices and deployment models for new video systems, while considering new implementation paradigms of hardware accelerators, parallelism for heterogeneous multi- and many-core systems, and systems with long life-cycles. Particular emphasis is given to the current video encoding industry standard H.264/AVC, and one of the latest video encoders (High Efficiency Video Coding, HEVC).
Efficient Processing of Deep Neural Networks
Efficient Processing of Deep Neural Networks - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Efficient Processing of Deep Neural Networks write by Vivienne Sze. This book was released on 2022-05-31. Efficient Processing of Deep Neural Networks available in PDF, EPUB and Kindle. 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.
Research Infrastructures for Hardware Accelerators
Research Infrastructures for Hardware Accelerators - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Research Infrastructures for Hardware Accelerators write by Yakun Sophia Shao. This book was released on 2022-05-31. Research Infrastructures for Hardware Accelerators available in PDF, EPUB and Kindle. Hardware acceleration in the form of customized datapath and control circuitry tuned to specific applications has gained popularity for its promise to utilize transistors more efficiently. Historically, the computer architecture community has focused on general-purpose processors, and extensive research infrastructure has been developed to support research efforts in this domain. Envisioning future computing systems with a diverse set of general-purpose cores and accelerators, computer architects must add accelerator-related research infrastructures to their toolboxes to explore future heterogeneous systems. This book serves as a primer for the field, as an overview of the vast literature on accelerator architectures and their design flows, and as a resource guidebook for researchers working in related areas.
