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Author: Ji Lin (Researcher in computer science)
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Tiny machine learning on IoT devices based on microcontroller units (MCUs) enables various real-world applications (e.g., keyword spotting, anomaly detection). However, deploying deep learning models to MCUs is challenging due to the limited memory size: the memory of microcontrollers is 2-3 orders of magnitude smaller even than mobile phones. In this thesis, we study efficient algorithms and systems for tiny-scale deep learning. We propose MCUNet, a framework that jointly designs the efficient neural architecture (TinyNAS) and the lightweight inference engine (TinyEngine), enabling ImageNet-scale inference on microcontrollers. TinyNAS adopts a two-stage neural architecture search approach that first optimizes the search space to fit the resource constraints, then specializes the network architecture in the optimized search space. TinyNAS can automatically handle diverse constraints (i.e. device, latency, energy, memory) under low search costs. TinyNAS is co-designed with TinyEngine, a memory-efficient inference library to expand the search space and fit a larger model. TinyEngine adapts the memory scheduling according to the overall network topology rather than layer-wise optimization, reducing the memory usage by 3.4x, and accelerating the inference by 1.7-3.3x compared to TF-Lite Micro and CMSIS-NN. For vision applications on MCUs, we diagnosed and found that existing convolutional neural network (CNN) designs have an imbalanced peak memory distribution: the first several layers have much higher peak memory usage than the rest of the network. Based on the observation, we further extend the framework to support patch-based inference to break the memory bottleneck of the initial stage. MCUNet is the first to achieves>70% ImageNet top1 accuracy on an off-the-shelf commercial microcontroller, using 3.5x less SRAM and 5.7x less Flash compared to quantized MobileNetV2 and ResNet-18. On visual & audio wake words tasks, MCUNet achieves state-of-the-art accuracy and runs 2.4- 3.4x faster than MobileNetV2 and ProxylessNAS-based solutions with 3.7-4.1x smaller peak SRAM. Our study suggests that the era of always-on tiny machine learning on IoT devices has arrived.
Author: Ji Lin (Researcher in computer science)
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
Tiny machine learning on IoT devices based on microcontroller units (MCUs) enables various real-world applications (e.g., keyword spotting, anomaly detection). However, deploying deep learning models to MCUs is challenging due to the limited memory size: the memory of microcontrollers is 2-3 orders of magnitude smaller even than mobile phones. In this thesis, we study efficient algorithms and systems for tiny-scale deep learning. We propose MCUNet, a framework that jointly designs the efficient neural architecture (TinyNAS) and the lightweight inference engine (TinyEngine), enabling ImageNet-scale inference on microcontrollers. TinyNAS adopts a two-stage neural architecture search approach that first optimizes the search space to fit the resource constraints, then specializes the network architecture in the optimized search space. TinyNAS can automatically handle diverse constraints (i.e. device, latency, energy, memory) under low search costs. TinyNAS is co-designed with TinyEngine, a memory-efficient inference library to expand the search space and fit a larger model. TinyEngine adapts the memory scheduling according to the overall network topology rather than layer-wise optimization, reducing the memory usage by 3.4x, and accelerating the inference by 1.7-3.3x compared to TF-Lite Micro and CMSIS-NN. For vision applications on MCUs, we diagnosed and found that existing convolutional neural network (CNN) designs have an imbalanced peak memory distribution: the first several layers have much higher peak memory usage than the rest of the network. Based on the observation, we further extend the framework to support patch-based inference to break the memory bottleneck of the initial stage. MCUNet is the first to achieves>70% ImageNet top1 accuracy on an off-the-shelf commercial microcontroller, using 3.5x less SRAM and 5.7x less Flash compared to quantized MobileNetV2 and ResNet-18. On visual & audio wake words tasks, MCUNet achieves state-of-the-art accuracy and runs 2.4- 3.4x faster than MobileNetV2 and ProxylessNAS-based solutions with 3.7-4.1x smaller peak SRAM. Our study suggests that the era of always-on tiny machine learning on IoT devices has arrived.
Author: Pete Warden
Publisher: O'Reilly Media
ISBN: 1492052019
Category : Computers
Languages : en
Pages : 504
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Book Description
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
Author: Vivienne Sze
Publisher: Springer Nature
ISBN: 3031017668
Category : Technology & Engineering
Languages : en
Pages : 254
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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.
Author: Bert Moons
Publisher: Springer
ISBN: 3319992236
Category : Technology & Engineering
Languages : en
Pages : 206
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Book Description
This book covers algorithmic and hardware implementation techniques to enable embedded deep learning. The authors describe synergetic design approaches on the application-, algorithmic-, computer architecture-, and circuit-level that will help in achieving the goal of reducing the computational cost of deep learning algorithms. The impact of these techniques is displayed in four silicon prototypes for embedded deep learning. Gives a wide overview of a series of effective solutions for energy-efficient neural networks on battery constrained wearable devices; Discusses the optimization of neural networks for embedded deployment on all levels of the design hierarchy – applications, algorithms, hardware architectures, and circuits – supported by real silicon prototypes; Elaborates on how to design efficient Convolutional Neural Network processors, exploiting parallelism and data-reuse, sparse operations, and low-precision computations; Supports the introduced theory and design concepts by four real silicon prototypes. The physical realization’s implementation and achieved performances are discussed elaborately to illustrated and highlight the introduced cross-layer design concepts.
Author: Song Guo
Publisher: CRC Press
ISBN: 100078035X
Category : Computers
Languages : en
Pages : 268
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Book Description
This book aims at the tiny machine learning (TinyML) software and hardware synergy for edge intelligence applications. It presents on-device learning techniques covering model-level neural network design, algorithm-level training optimization, and hardware-level instruction acceleration. Analyzing the limitations of conventional in-cloud computing would reveal that on-device learning is a promising research direction to meet the requirements of edge intelligence applications. As to the cutting-edge research of TinyML, implementing a high-efficiency learning framework and enabling system-level acceleration is one of the most fundamental issues. This book presents a comprehensive discussion of the latest research progress and provides system-level insights on designing TinyML frameworks, including neural network design, training algorithm optimization and domain-specific hardware acceleration. It identifies the main challenges when deploying TinyML tasks in the real world and guides the researchers to deploy a reliable learning system. This volume will be of interest to students and scholars in the field of edge intelligence, especially to those with sufficient professional Edge AI skills. It will also be an excellent guide for researchers to implement high-performance TinyML systems.
Author: Vincenzo Piuri
Publisher: Academic Press
ISBN: 0128232684
Category : Computers
Languages : en
Pages : 308
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Book Description
Trends in Deep Learning Methodologies: Algorithms, Applications, and Systems covers deep learning approaches such as neural networks, deep belief networks, recurrent neural networks, convolutional neural networks, deep auto-encoder, and deep generative networks, which have emerged as powerful computational models. Chapters elaborate on these models which have shown significant success in dealing with massive data for a large number of applications, given their capacity to extract complex hidden features and learn efficient representation in unsupervised settings. Chapters investigate deep learning-based algorithms in a variety of application, including biomedical and health informatics, computer vision, image processing, and more. In recent years, many powerful algorithms have been developed for matching patterns in data and making predictions about future events. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive algorithms to handle more data. Deep learning methods can deal with multiple levels of representation in which the system learns to abstract higher level representations of raw data. Earlier, it was a common requirement to have a domain expert to develop a specific model for each specific application, however, recent advancements in representation learning algorithms allow researchers across various subject domains to automatically learn the patterns and representation of the given data for the development of specific models. Provides insights into the theory, algorithms, implementation and the application of deep learning techniques Covers a wide range of applications of deep learning across smart healthcare and smart engineering Investigates the development of new models and how they can be exploited to find appropriate solutions
Author: Martin Ford
Publisher: Packt Publishing Ltd
ISBN: 178913126X
Category : Computers
Languages : en
Pages : 540
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Book Description
Financial Times Best Books of the Year 2018 TechRepublic Top Books Every Techie Should Read Book Description How will AI evolve and what major innovations are on the horizon? What will its impact be on the job market, economy, and society? What is the path toward human-level machine intelligence? What should we be concerned about as artificial intelligence advances? Architects of Intelligence contains a series of in-depth, one-to-one interviews where New York Times bestselling author, Martin Ford, uncovers the truth behind these questions from some of the brightest minds in the Artificial Intelligence community. Martin has wide-ranging conversations with twenty-three of the world's foremost researchers and entrepreneurs working in AI and robotics: Demis Hassabis (DeepMind), Ray Kurzweil (Google), Geoffrey Hinton (Univ. of Toronto and Google), Rodney Brooks (Rethink Robotics), Yann LeCun (Facebook) , Fei-Fei Li (Stanford and Google), Yoshua Bengio (Univ. of Montreal), Andrew Ng (AI Fund), Daphne Koller (Stanford), Stuart Russell (UC Berkeley), Nick Bostrom (Univ. of Oxford), Barbara Grosz (Harvard), David Ferrucci (Elemental Cognition), James Manyika (McKinsey), Judea Pearl (UCLA), Josh Tenenbaum (MIT), Rana el Kaliouby (Affectiva), Daniela Rus (MIT), Jeff Dean (Google), Cynthia Breazeal (MIT), Oren Etzioni (Allen Institute for AI), Gary Marcus (NYU), and Bryan Johnson (Kernel). Martin Ford is a prominent futurist, and author of Financial Times Business Book of the Year, Rise of the Robots. He speaks at conferences and companies around the world on what AI and automation might mean for the future. Meet the minds behind the AI superpowers as they discuss the science, business and ethics of modern artificial intelligence. Read James Manyika’s thoughts on AI analytics, Geoffrey Hinton’s breakthroughs in AI programming and development, and Rana el Kaliouby’s insights into AI marketing. This AI book collects the opinions of the luminaries of the AI business, such as Stuart Russell (coauthor of the leading AI textbook), Rodney Brooks (a leader in AI robotics), Demis Hassabis (chess prodigy and mind behind AlphaGo), and Yoshua Bengio (leader in deep learning) to complete your AI education and give you an AI advantage in 2019 and the future.
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: Murad Khan
Publisher: Springer
ISBN: 9811334595
Category : Computers
Languages : en
Pages : 79
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Book Description
This book presents deep learning techniques, concepts, and algorithms to classify and analyze big data. Further, it offers an introductory level understanding of the new programming languages and tools used to analyze big data in real-time, such as Hadoop, SPARK, and GRAPHX. Big data analytics using traditional techniques face various challenges, such as fast, accurate and efficient processing of big data in real-time. In addition, the Internet of Things is progressively increasing in various fields, like smart cities, smart homes, and e-health. As the enormous number of connected devices generate huge amounts of data every day, we need sophisticated algorithms to deal, organize, and classify this data in less processing time and space. Similarly, existing techniques and algorithms for deep learning in big data field have several advantages thanks to the two main branches of the deep learning, i.e. convolution and deep belief networks. This book offers insights into these techniques and applications based on these two types of deep learning. Further, it helps students, researchers, and newcomers understand big data analytics based on deep learning approaches. It also discusses various machine learning techniques in concatenation with the deep learning paradigm to support high-end data processing, data classifications, and real-time data processing issues. The classification and presentation are kept quite simple to help the readers and students grasp the basics concepts of various deep learning paradigms and frameworks. It mainly focuses on theory rather than the mathematical background of the deep learning concepts. The book consists of 5 chapters, beginning with an introductory explanation of big data and deep learning techniques, followed by integration of big data and deep learning techniques and lastly the future directions.
Author: Shai Shalev-Shwartz
Publisher: Cambridge University Press
ISBN: 1107057132
Category : Computers
Languages : en
Pages : 415
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Book Description
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
