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Author: Zixuan Jiang
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Machine learning enables the extraction of knowledge from data and decision-making without explicit programming, achieving great success and revolutionizing many fields. These successes can be attributed to the continuous advancements in machine learning software and hardware, which have expanded the boundaries and facilitated breakthroughs in diverse applications. The machine learning software stack is a comprehensive collection of components used to solve problems with machine learning algorithms. It encompasses problem definitions, data processing, model and method designs, software frameworks, libraries, code optimization, and system management. This stack supports the entire life cycle of a machine learning project. The software stack allows the community to stand on the shoulders of previous great work and push the limit of machine learning, fostering innovation and enabling broader adoption of machine learning techniques in academia and industry. The software stack is usually divided into algorithm and compilation with distinct design principles. Algorithm design prioritizes task-related performance, while compilation focuses on execution time and resource consumption on hardware devices. Maintaining arithmetic equivalence is optional in algorithm design, but compulsory in compilation to ensure consistent results. The compilation is closer to hardware than algorithm design. Compilation engineers optimize for hardware specifications, while algorithm developers usually do not prioritize hardware-friendliness. Opportunities to enhance hardware efficiency exist in algorithm and compilation designs, as well as their interplay. Despite extensive innovations and improvements, efficiency in the machine learning software stack is a continuing challenge. Algorithm design proposes efficient model architectures and learning algorithms, while compilation design optimizes computation graphs and simplifies operations. However, there is still a gap between the demand for efficiency and the current solutions, driven by rapidly growing workloads, limited resources in specific machine learning applications, and the need for cross-layer design. Addressing these challenges requires interdisciplinary research and collaboration. Improving efficiency in the machine learning software stack will optimize performance and enhance the accessibility and applicability of machine learning technologies. In this dissertation, we focus on addressing these efficiency challenges from the perspectives of machine learning algorithms and compilation. We introduce three novel improvements that enhance the efficiency of mainstream machine learning algorithms. Firstly, effective gradient matching for dataset condensation generates a small insightful dataset, accelerating training and other related tasks. Additionally, NormSoftmax proposes to append a normalization layer to achieve fast and stable training in Transformers and classification models. Lastly, mixed precision hardware-aware neural architecture search combines mixed-precision quantization, neural architecture search, and hardware energy efficiency, resulting in significantly more efficient neural networks than using a single method. However, algorithmic efficiency alone is insufficient to fully exploit the potential in the machine learning software stack. We delve into and optimize the compilation processes with three techniques. Firstly, we simplify the layer normalization in the influential Transformers, obtaining two equivalent and efficient Transformer variants with alternative normalization types. Our proposed variants enable efficient training and inference of popular models like GPT and ViT. Secondly, we formulate and solve the scheduling problem for reversible neural architectures, finding the optimal training schedule that fully leverages the computation and memory resources on hardware accelerators. Lastly, optimizer fusion allows users to accelerate the training process in the eager execution mode of machine learning frameworks. It leverages the better locality on hardware and parallelism in the computation graphs. Throughout the dissertation, we emphasize the integration of efficient algorithms and compilation into a cohesive machine learning software stack. We also consider hardware properties to provide hardware-friendly software designs. We demonstrate the effectiveness of the proposed methods in algorithm and compilation through extensive experiments. Our approaches effectively reduce the time and energy required for both training and inference. Ultimately, our methods have the potential to empower machine learning practitioners and researchers to build more efficient, powerful, robust, scalable, and accessible machine learning solutions
Author: Zixuan Jiang
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
Machine learning enables the extraction of knowledge from data and decision-making without explicit programming, achieving great success and revolutionizing many fields. These successes can be attributed to the continuous advancements in machine learning software and hardware, which have expanded the boundaries and facilitated breakthroughs in diverse applications. The machine learning software stack is a comprehensive collection of components used to solve problems with machine learning algorithms. It encompasses problem definitions, data processing, model and method designs, software frameworks, libraries, code optimization, and system management. This stack supports the entire life cycle of a machine learning project. The software stack allows the community to stand on the shoulders of previous great work and push the limit of machine learning, fostering innovation and enabling broader adoption of machine learning techniques in academia and industry. The software stack is usually divided into algorithm and compilation with distinct design principles. Algorithm design prioritizes task-related performance, while compilation focuses on execution time and resource consumption on hardware devices. Maintaining arithmetic equivalence is optional in algorithm design, but compulsory in compilation to ensure consistent results. The compilation is closer to hardware than algorithm design. Compilation engineers optimize for hardware specifications, while algorithm developers usually do not prioritize hardware-friendliness. Opportunities to enhance hardware efficiency exist in algorithm and compilation designs, as well as their interplay. Despite extensive innovations and improvements, efficiency in the machine learning software stack is a continuing challenge. Algorithm design proposes efficient model architectures and learning algorithms, while compilation design optimizes computation graphs and simplifies operations. However, there is still a gap between the demand for efficiency and the current solutions, driven by rapidly growing workloads, limited resources in specific machine learning applications, and the need for cross-layer design. Addressing these challenges requires interdisciplinary research and collaboration. Improving efficiency in the machine learning software stack will optimize performance and enhance the accessibility and applicability of machine learning technologies. In this dissertation, we focus on addressing these efficiency challenges from the perspectives of machine learning algorithms and compilation. We introduce three novel improvements that enhance the efficiency of mainstream machine learning algorithms. Firstly, effective gradient matching for dataset condensation generates a small insightful dataset, accelerating training and other related tasks. Additionally, NormSoftmax proposes to append a normalization layer to achieve fast and stable training in Transformers and classification models. Lastly, mixed precision hardware-aware neural architecture search combines mixed-precision quantization, neural architecture search, and hardware energy efficiency, resulting in significantly more efficient neural networks than using a single method. However, algorithmic efficiency alone is insufficient to fully exploit the potential in the machine learning software stack. We delve into and optimize the compilation processes with three techniques. Firstly, we simplify the layer normalization in the influential Transformers, obtaining two equivalent and efficient Transformer variants with alternative normalization types. Our proposed variants enable efficient training and inference of popular models like GPT and ViT. Secondly, we formulate and solve the scheduling problem for reversible neural architectures, finding the optimal training schedule that fully leverages the computation and memory resources on hardware accelerators. Lastly, optimizer fusion allows users to accelerate the training process in the eager execution mode of machine learning frameworks. It leverages the better locality on hardware and parallelism in the computation graphs. Throughout the dissertation, we emphasize the integration of efficient algorithms and compilation into a cohesive machine learning software stack. We also consider hardware properties to provide hardware-friendly software designs. We demonstrate the effectiveness of the proposed methods in algorithm and compilation through extensive experiments. Our approaches effectively reduce the time and energy required for both training and inference. Ultimately, our methods have the potential to empower machine learning practitioners and researchers to build more efficient, powerful, robust, scalable, and accessible machine learning solutions
Author: Andres Rodriguez
Publisher: Springer Nature
ISBN: 3031017692
Category : Technology & Engineering
Languages : en
Pages : 245
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Book Description
This book describes deep learning systems: the algorithms, compilers, and processor components to efficiently train and deploy deep learning models for commercial applications. The exponential growth in computational power is slowing at a time when the amount of compute consumed by state-of-the-art deep learning (DL) workloads is rapidly growing. Model size, serving latency, and power constraints are a significant challenge in the deployment of DL models for many applications. Therefore, it is imperative to codesign algorithms, compilers, and hardware to accelerate advances in this field with holistic system-level and algorithm solutions that improve performance, power, and efficiency. Advancing DL systems generally involves three types of engineers: (1) data scientists that utilize and develop DL algorithms in partnership with domain experts, such as medical, economic, or climate scientists; (2) hardware designers that develop specialized hardware to accelerate the components in the DL models; and (3) performance and compiler engineers that optimize software to run more efficiently on a given hardware. Hardware engineers should be aware of the characteristics and components of production and academic models likely to be adopted by industry to guide design decisions impacting future hardware. Data scientists should be aware of deployment platform constraints when designing models. Performance engineers should support optimizations across diverse models, libraries, and hardware targets. The purpose of this book is to provide a solid understanding of (1) the design, training, and applications of DL algorithms in industry; (2) the compiler techniques to map deep learning code to hardware targets; and (3) the critical hardware features that accelerate DL systems. This book aims to facilitate co-innovation for the advancement of DL systems. It is written for engineers working in one or more of these areas who seek to understand the entire system stack in order to better collaborate with engineers working in other parts of the system stack. The book details advancements and adoption of DL models in industry, explains the training and deployment process, describes the essential hardware architectural features needed for today's and future models, and details advances in DL compilers to efficiently execute algorithms across various hardware targets. Unique in this book is the holistic exposition of the entire DL system stack, the emphasis on commercial applications, and the practical techniques to design models and accelerate their performance. The author is fortunate to work with hardware, software, data scientist, and research teams across many high-technology companies with hyperscale data centers. These companies employ many of the examples and methods provided throughout the book.
Author: Steven Bell
Publisher: Springer Nature
ISBN: 3031017587
Category : Technology & Engineering
Languages : en
Pages : 89
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Book Description
Most emerging applications in imaging and machine learning must perform immense amounts of computation while holding to strict limits on energy and power. To meet these goals, architects are building increasingly specialized compute engines tailored for these specific tasks. The resulting computer systems are heterogeneous, containing multiple processing cores with wildly different execution models. Unfortunately, the cost of producing this specialized hardware—and the software to control it—is astronomical. Moreover, the task of porting algorithms to these heterogeneous machines typically requires that the algorithm be partitioned across the machine and rewritten for each specific architecture, which is time consuming and prone to error. Over the last several years, the authors have approached this problem using domain-specific languages (DSLs): high-level programming languages customized for specific domains, such as database manipulation, machine learning, or image processing. By giving up generality, these languages are able to provide high-level abstractions to the developer while producing high-performance output. The purpose of this book is to spur the adoption and the creation of domain-specific languages, especially for the task of creating hardware designs. In the first chapter, a short historical journey explains the forces driving computer architecture today. Chapter 2 describes the various methods for producing designs for accelerators, outlining the push for more abstraction and the tools that enable designers to work at a higher conceptual level. From there, Chapter 3 provides a brief introduction to image processing algorithms and hardware design patterns for implementing them. Chapters 4 and 5 describe and compare Darkroom and Halide, two domain-specific languages created for image processing that produce high-performance designs for both FPGAs and CPUs from the same source code, enabling rapid design cycles and quick porting of algorithms. The final section describes how the DSL approach also simplifies the problem of interfacing between application code and the accelerator by generating the driver stack in addition to the accelerator configuration. This book should serve as a useful introduction to domain-specialized computing for computer architecture students and as a primer on domain-specific languages and image processing hardware for those with more experience in the field.
Author: Suman Lata Tripathi
Publisher: John Wiley & Sons
ISBN: 1394185847
Category : Computers
Languages : en
Pages : 277
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Book Description
EXPLAINABLE MACHINE LEARNING MODELS AND ARCHITECTURES This cutting-edge new volume covers the hardware architecture implementation, the software implementation approach, and the efficient hardware of machine learning applications. Machine learning and deep learning modules are now an integral part of many smart and automated systems where signal processing is performed at different levels. Signal processing in the form of text, images, or video needs large data computational operations at the desired data rate and accuracy. Large data requires more use of integrated circuit (IC) area with embedded bulk memories that further lead to more IC area. Trade-offs between power consumption, delay and IC area are always a concern of designers and researchers. New hardware architectures and accelerators are needed to explore and experiment with efficient machine-learning models. Many real-time applications like the processing of biomedical data in healthcare, smart transportation, satellite image analysis, and IoT-enabled systems have a lot of scope for improvements in terms of accuracy, speed, computational powers, and overall power consumption. This book deals with the efficient machine and deep learning models that support high-speed processors with reconfigurable architectures like graphic processing units (GPUs) and field programmable gate arrays (FPGAs), or any hybrid system. Whether for the veteran engineer or scientist working in the field or laboratory, or the student or academic, this is a must-have for any library.
Author: James Brodman
Publisher: Springer
ISBN: 3319174738
Category : Computers
Languages : en
Pages : 401
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Book Description
This book constitutes the thoroughly refereed post-conference proceedings of the 27th International Workshop on Languages and Compilers for Parallel Computing, LCPC 2014, held in Hillsboro, OR, USA, in September 2014. The 25 revised full papers were carefully reviewed and selected from 39 submissions. The papers are organized in topical sections on accelerator programming; algorithms for parallelism; compilers; debugging; vectorization.
Author: Jason Bell
Publisher: John Wiley & Sons
ISBN: 1119642191
Category : Mathematics
Languages : en
Pages : 487
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Book Description
Dig deep into the data with a hands-on guide to machine learning with updated examples and more! Machine Learning: Hands-On for Developers and Technical Professionals provides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by developers and technical professionals. The book contains a breakdown of each ML variant, explaining how it works and how it is used within certain industries, allowing readers to incorporate the presented techniques into their own work as they follow along. A core tenant of machine learning is a strong focus on data preparation, and a full exploration of the various types of learning algorithms illustrates how the proper tools can help any developer extract information and insights from existing data. The book includes a full complement of Instructor's Materials to facilitate use in the classroom, making this resource useful for students and as a professional reference. At its core, machine learning is a mathematical, algorithm-based technology that forms the basis of historical data mining and modern big data science. Scientific analysis of big data requires a working knowledge of machine learning, which forms predictions based on known properties learned from training data. Machine Learning is an accessible, comprehensive guide for the non-mathematician, providing clear guidance that allows readers to: Learn the languages of machine learning including Hadoop, Mahout, and Weka Understand decision trees, Bayesian networks, and artificial neural networks Implement Association Rule, Real Time, and Batch learning Develop a strategic plan for safe, effective, and efficient machine learning By learning to construct a system that can learn from data, readers can increase their utility across industries. Machine learning sits at the core of deep dive data analysis and visualization, which is increasingly in demand as companies discover the goldmine hiding in their existing data. For the tech professional involved in data science, Machine Learning: Hands-On for Developers and Technical Professionals provides the skills and techniques required to dig deeper.
Author: Rosey Press
Publisher: Independently Published
ISBN:
Category : Computers
Languages : en
Pages : 0
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Book Description
Machine learning frameworks are essential tools for anyone working in the field of artificial intelligence and data science. These frameworks provide a foundation for building and deploying machine learning models, allowing users to take advantage of pre-built algorithms and libraries to streamline the development process. In this subchapter, we will explore what machine learning frameworks are, how they work, and why they are important for anyone looking to work in the field of machine learning. Machine learning frameworks are software libraries that provide developers with a set of tools and algorithms for building and training machine learning models. These frameworks are designed to simplify the process of developing machine learning applications by providing a high-level interface that abstracts away many of the complex details of machine learning algorithms. By using a machine learning framework, developers can focus on building and testing their models rather than getting bogged down in the technical details of algorithm implementation. There are many different machine learning frameworks available, each with its own strengths and weaknesses. Some frameworks are designed for specific types of machine learning tasks, such as deep learning, reinforcement learning, natural language processing, computer vision, transfer learning, Bayesian machine learning, generative adversarial networks (GANs), AutoML, federated learning, and time series analysis. By choosing the right framework for their specific needs, developers can accelerate the development process and build more robust and accurate machine learning models. One of the key benefits of using a machine learning framework is the ability to leverage pre-built algorithms and libraries. These libraries contain implementations of popular machine learning algorithms, such as neural networks, decision trees, support vector machines, and clustering algorithms, making it easy for developers to experiment with different algorithms and techniques. By using a machine learning framework, developers can save time and effort by not having to reinvent the wheel and can focus on building innovative and impactful machine learning applications. In addition to providing pre-built algorithms, machine learning frameworks also offer a range of tools and utilities for data preprocessing, model evaluation, and deployment. These tools can help developers clean and prepare their data, evaluate the performance of their models, and deploy their models in production environments. By using a machine learning framework, developers can streamline the entire machine learning pipeline, from data collection and preprocessing to model training and deployment, making it easier to build and deploy machine learning applications at scale. Overall, machine learning frameworks play a crucial role in the development of machine learning applications, providing developers with the tools and resources they need to build accurate and efficient machine learning models.
Author: Christopher Bourez
Publisher: Packt Publishing Ltd
ISBN: 1786463059
Category : Computers
Languages : en
Pages : 300
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Book Description
Develop deep neural networks in Theano with practical code examples for image classification, machine translation, reinforcement agents, or generative models. About This Book Learn Theano basics and evaluate your mathematical expressions faster and in an efficient manner Learn the design patterns of deep neural architectures to build efficient and powerful networks on your datasets Apply your knowledge to concrete fields such as image classification, object detection, chatbots, machine translation, reinforcement agents, or generative models. Who This Book Is For This book is indented to provide a full overview of deep learning. From the beginner in deep learning and artificial intelligence, to the data scientist who wants to become familiar with Theano and its supporting libraries, or have an extended understanding of deep neural nets. Some basic skills in Python programming and computer science will help, as well as skills in elementary algebra and calculus. What You Will Learn Get familiar with Theano and deep learning Provide examples in supervised, unsupervised, generative, or reinforcement learning. Discover the main principles for designing efficient deep learning nets: convolutions, residual connections, and recurrent connections. Use Theano on real-world computer vision datasets, such as for digit classification and image classification. Extend the use of Theano to natural language processing tasks, for chatbots or machine translation Cover artificial intelligence-driven strategies to enable a robot to solve games or learn from an environment Generate synthetic data that looks real with generative modeling Become familiar with Lasagne and Keras, two frameworks built on top of Theano In Detail This book offers a complete overview of Deep Learning with Theano, a Python-based library that makes optimizing numerical expressions and deep learning models easy on CPU or GPU. The book provides some practical code examples that help the beginner understand how easy it is to build complex neural networks, while more experimented data scientists will appreciate the reach of the book, addressing supervised and unsupervised learning, generative models, reinforcement learning in the fields of image recognition, natural language processing, or game strategy. The book also discusses image recognition tasks that range from simple digit recognition, image classification, object localization, image segmentation, to image captioning. Natural language processing examples include text generation, chatbots, machine translation, and question answering. The last example deals with generating random data that looks real and solving games such as in the Open-AI gym. At the end, this book sums up the best -performing nets for each task. While early research results were based on deep stacks of neural layers, in particular, convolutional layers, the book presents the principles that improved the efficiency of these architectures, in order to help the reader build new custom nets. Style and approach It is an easy-to-follow example book that teaches you how to perform fast, efficient computations in Python. Starting with the very basics-NumPy, installing Theano, this book will take you to the smooth journey of implementing Theano for advanced computations for machine learning and deep learning.
Author: Patanjali Kashyap
Publisher: Apress
ISBN: 1484229886
Category : Computers
Languages : en
Pages : 381
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Book Description
Take a deep dive into the concepts of machine learning as they apply to contemporary business and management. You will learn how machine learning techniques are used to solve fundamental and complex problems in society and industry. Machine Learning for Decision Makers serves as an excellent resource for establishing the relationship of machine learning with IoT, big data, and cognitive and cloud computing to give you an overview of how these modern areas of computing relate to each other. This book introduces a collection of the most important concepts of machine learning and sets them in context with other vital technologies that decision makers need to know about. These concepts span the process from envisioning the problem to applying machine-learning techniques to your particular situation. This discussion also provides an insight to help deploy the results to improve decision-making. The book uses case studies and jargon busting to help you grasp the theory of machine learning quickly. You'll soon gain the big picture of machine learning and how it fits with other cutting-edge IT services. This knowledge will give you confidence in your decisions for the future of your business. What You Will Learn Discover the machine learning, big data, and cloud and cognitive computing technology stack Gain insights into machine learning concepts and practices Understand business and enterprise decision-making using machine learning Absorb machine-learning best practices Who This Book Is For Managers tasked with making key decisions who want to learn how and when machine learning and related technologies can help them.
Author: Marcello La Rocca
Publisher: Simon and Schuster
ISBN: 1638350221
Category : Computers
Languages : en
Pages : 768
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Book Description
Advanced Algorithms and Data Structures introduces a collection of algorithms for complex programming challenges in data analysis, machine learning, and graph computing. Summary As a software engineer, you’ll encounter countless programming challenges that initially seem confusing, difficult, or even impossible. Don’t despair! Many of these “new” problems already have well-established solutions. Advanced Algorithms and Data Structures teaches you powerful approaches to a wide range of tricky coding challenges that you can adapt and apply to your own applications. Providing a balanced blend of classic, advanced, and new algorithms, this practical guide upgrades your programming toolbox with new perspectives and hands-on techniques. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Can you improve the speed and efficiency of your applications without investing in new hardware? Well, yes, you can: Innovations in algorithms and data structures have led to huge advances in application performance. Pick up this book to discover a collection of advanced algorithms that will make you a more effective developer. About the book Advanced Algorithms and Data Structures introduces a collection of algorithms for complex programming challenges in data analysis, machine learning, and graph computing. You’ll discover cutting-edge approaches to a variety of tricky scenarios. You’ll even learn to design your own data structures for projects that require a custom solution. What's inside Build on basic data structures you already know Profile your algorithms to speed up application Store and query strings efficiently Distribute clustering algorithms with MapReduce Solve logistics problems using graphs and optimization algorithms About the reader For intermediate programmers. About the author Marcello La Rocca is a research scientist and a full-stack engineer. His focus is on optimization algorithms, genetic algorithms, machine learning, and quantum computing. Table of Contents 1 Introducing data structures PART 1 IMPROVING OVER BASIC DATA STRUCTURES 2 Improving priority queues: d-way heaps 3 Treaps: Using randomization to balance binary search trees 4 Bloom filters: Reducing the memory for tracking content 5 Disjoint sets: Sub-linear time processing 6 Trie, radix trie: Efficient string search 7 Use case: LRU cache PART 2 MULTIDEMENSIONAL QUERIES 8 Nearest neighbors search 9 K-d trees: Multidimensional data indexing 10 Similarity Search Trees: Approximate nearest neighbors search for image retrieval 11 Applications of nearest neighbor search 12 Clustering 13 Parallel clustering: MapReduce and canopy clustering PART 3 PLANAR GRAPHS AND MINIMUM CROSSING NUMBER 14 An introduction to graphs: Finding paths of minimum distance 15 Graph embeddings and planarity: Drawing graphs with minimal edge intersections 16 Gradient descent: Optimization problems (not just) on graphs 17 Simulated annealing: Optimization beyond local minima 18 Genetic algorithms: Biologically inspired, fast-converging optimization
