Resource Efficient Deep Learning

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Resource-efficient Deep Learning

Author : Dongkuan Xu
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (134 download)

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Book Synopsis Resource-efficient Deep Learning by : Dongkuan Xu

Download or read book Resource-efficient Deep Learning written by Dongkuan Xu and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The phenomenal success of deep learning in the past decade has been mostly driven by the construction of increasingly large deep neural network models. These models usually impose an ideal assumption that there are sufficient resources, including large-scale parameters, sufficient data, and massive computation, for the optimization. However, this assumption usually fails in real-world scenarios. For example, computer memory may be limited as in edge devices, large-scale data are difficult to obtain due to expensive costs and privacy constraints, and computational power is constrained as in most university labs. As a result, these resource discrepancy issues have hindered the democratization of deep learning techniques in many AI applications, and the development of efficient deep learning methods that can adapt to different resource constraints is of great importance. In this dissertation, I will present my Ph.D. research concerned with the aforementioned resource discrepancy issues to free AI from the parameter-data-computation hungry beast in three threads. The first thread focuses on data efficiency in deep learning technologies. This thread extends advances in deep learning to scenarios with small, sensitive, or unlabeled data, accelerating the acceptance and adoption of AI in real-world applications. In particular, I study self-supervised learning to remove the dependency on labels, few-shot learning to free model from a large number of samples, and and attentive learning to take full advantage of heterogeneous information sources. The second thread of my work focuses on advances of parameter efficiency in deep learning technologies, which enable us to democratize powerful deep learning models at scale to bridge computer memory divide and improve the adaptability of models in dynamic environments. I study network sparsity, i.e., the technology to prune networks, and network modularity, i.e., the technology to modularize neural networks into multiple modules, each of which is a function with its own parameters. The third thread focuses on computation efficiency of deep learning models, from inference to training, reducing the energy consumption of models, promoting environmental sustainability, and complementing data efficiency. More specifically, I study task-agnostic model compression, the task of generating efficient compressed models without utilizing the downstream task label information, avoiding the repetitive compression process, which saves much training cost.

Resource-efficient Execution of Deep Learning Computations

Author : Deepak Narayanan
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.4/5 (944 download)

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Book Synopsis Resource-efficient Execution of Deep Learning Computations by : Deepak Narayanan

Download or read book Resource-efficient Execution of Deep Learning Computations written by Deepak Narayanan and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning models have enabled state-of-the-art results across a broad range of applications. Training these models, however, is extremely time- and resource-intensive, taking weeks on clusters with thousands of expensive accelerators in the extreme case. As Moore's Law slows down, numerous parallel accelerators have been introduced to meet this new computational demand. This dissertation shows how model- and hardware-aware optimizations in software systems can help intelligently navigate this heterogeneity. In particular, it demonstrates how careful automated scheduling of computation across levels of the software stack can be used to perform distributed training and resource allocation more efficiently. In the first part of this dissertation, we study pipelining, a technique commonly used as a performance optimization in various systems, as a way to perform more efficient distributed model training for both models with small training footprints and those with training footprints larger than the memory capacity of a single GPU. For certain types of models, pipeline parallelism can facilitate model training with lower communication overhead than previous methods. We introduce new strategies for pipeline parallelism, with different tradeoffs between training throughput, memory footprint, and weight update semantics; these outperform existing methods in certain settings. Pipeline parallelism can also be used in conjunction with other forms of parallelism, helping create a richer search space of parallelization strategies. By partitioning the training graph across accelerators in a model-aware way, pipeline parallelism combined with data parallelism can be up to 5x faster than data parallelism in isolation. We also use a principled combination of pipeline parallelism, tensor model parallelism, and data parallelism to efficiently scale training to language models with a trillion parameters on 3072 A100 GPUs (aggregate throughput of 502 petaFLOP/s, which is 52% of peak device throughput). In the second part of this dissertation, we show how heterogeneous compute resources (e.g., different GPU generations like NVIDIA K80 and V100 GPUs) in a shared cluster (either in a private deployment or in the public cloud) should be partitioned among multiple users to optimize objectives specified over one or more training jobs. By formulating existing policies as optimization problems over the allocation, and then using a concept we call effective throughput, policies can be extended to be heterogeneity-aware. A policy-agnostic scheduling mechanism then helps realize the heterogeneity-aware allocations returned by these policies in practice. We can improve various scheduling objectives, such as average completion time, makespan, or cloud computing resource cost, by up to 3.5x, using these heterogeneity-aware policies. Towards the end of this dissertation, we also touch on how the dynamic pricing information of spot instances can be plugged into this heterogeneity-aware policy framework to optimize cost objectives in the public cloud. This can help reduce cost compared to using more expensive on-demand instances alone.

Resource Efficient and Error Resilient Neural Networks

Author : Jeng-Hau Lin
Publisher :
ISBN 13 :
Total Pages : 142 pages
Book Rating : 4.:/5 (11 download)

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Book Synopsis Resource Efficient and Error Resilient Neural Networks by : Jeng-Hau Lin

Download or read book Resource Efficient and Error Resilient Neural Networks written by Jeng-Hau Lin and published by . This book was released on 2019 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: The entangled guardbands in terms of timing specification and energy budget ensure a system against faults, but the guardbands, meanwhile, impede the advance of a higher throughput and energy efficiency. To combat the over-designed guardbands in a system carrying out deep learning inference, we dive into the algorithmic demands and understand that the resource deficiency and hardware variation are the major reasons of the need of conservative guardbands. In modern convolutional neural networks (CNNs), the number of arithmetic operations for the inference could exceed tens of billions, which requires a sophisticated buffering mechanism to balance between resource utilization and throughput. In this case, the over-designed guardbands can seriously hinder system performance. On the other hand, timing errors can be incurred by the hardware variations including momentary voltage droops resulted from simultaneous switching noises, a gradually decreasing voltage level due to a limited battery, and the slow electron mobility incurred by the system power dissipation into heat. The timing errors propagating in a network can be a snowball in the beginning but ends up with a catastrophe in terms of a significant accuracy degradation. Knowing the need of guardbands originates from resource deficiency and timing errors, this dissertation focuses on cross-layer solutions to the problems of the high algorithmic demands incurred by deep learning methods and error vulnerability due to hardware variations. We begin with reviewing the methods and technologies proposed in the literature including weight encoding, filter decomposition, network pruning, efficient structure design, and precision quantizing. In the implementation of an FPGA accelerator for extreme-case quantization, binarized neural networks (BNN), we have realized more possible optimizations can be applied. Then, we extend BNN on the algorithmic layer with the binarized separable filters and proposed BCNNw/SF. Although the quantization and approximation benefit hardware efficiency to a certain extent, the optimal reduction or compression rate is still limited by the core of the conventional deep learning methods--convolution. We, thus, introduce the local binary pattern (LBP) to deep learning because of LBP’s low complexity yet high effectiveness. We name the new algorithm LBPNet, in which the feature maps are created with a similar fashion of the traditional LBP using comparisons. Our LBPNet can be trained with the forward-backward propagation algorithm to extract useful features for image classification. LBPNet accelerators have been implemented and optimized to verify their classification performance, processing throughput, and energy efficiency. We also demonstrate the error immunity of LBPNet to be the strongest compared with the subject MLP, CNN, and BCNN models since the classification accuracy of the LBPNet is decreased by only 10% and all the other models lose the classification ability when the timing error rate exceeds 0.01.

Efficient Processing of Deep Neural Networks

Author : Vivienne Sze
Publisher : Springer Nature
ISBN 13 : 3031017668
Total Pages : 254 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Efficient Processing of Deep Neural Networks by : Vivienne Sze

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.

Digital Technologies for a Resource Efficient Economy

Author : Ordóñez de Pablos, Patricia
Publisher : IGI Global
ISBN 13 :
Total Pages : 346 pages
Book Rating : 4.3/5 (693 download)

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Book Synopsis Digital Technologies for a Resource Efficient Economy by : Ordóñez de Pablos, Patricia

Download or read book Digital Technologies for a Resource Efficient Economy written by Ordóñez de Pablos, Patricia and published by IGI Global. This book was released on 2024-05-06 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt: In an era marked by escalating environmental concerns and the imperative for sustainable development, a pressing challenge looms large: the urgent need for transitioning towards circular and climate-neutral economies. As industries grapple with the complexities of achieving these critical milestones, Digital Technologies for a Resource Efficient Economy explores innovative conceptual frameworks, case studies, and empirical studies, seeking to unravel the relationship between clean technologies, digital innovation, and knowledge management. Positioned at the intersection of academia and real-world solutions, its insightful exploration engages academic scholars, researchers, industry players, policymakers, and stakeholders in a dynamic discourse on the challenges, opportunities, and trends shaping the path towards a net-zero world in Asia and beyond. Targeting a diverse audience that includes professors, policymakers, corporate leaders, and students, Digital Technologies for a Resource Efficient Economy becomes a cornerstone in the exploration of artificial intelligence, circular economy, clean energy, and other pivotal topics. By combining academic rigor with practical applications, the book becomes an indispensable resource for navigating the complexities of building resilient, inclusive, and green societies. With its recommended topics spanning a global spectrum, encompassing regions from Asia to the EU, USA, Latin America, Africa, and the Gulf Region, the book takes on a truly comprehensive approach. Seamlessly weaving together the intricacies of technology, innovation, and sustainable development, this book positions itself as a crucial guide for anyone invested in shaping a future where economies thrive in harmony with the environment.

Machine Learning for Future Wireless Communications

Author : Fa-Long Luo
Publisher : John Wiley & Sons
ISBN 13 : 1119562252
Total Pages : 490 pages
Book Rating : 4.1/5 (195 download)

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Book Synopsis Machine Learning for Future Wireless Communications by : Fa-Long Luo

Download or read book Machine Learning for Future Wireless Communications written by Fa-Long Luo and published by John Wiley & Sons. This book was released on 2020-02-10 with total page 490 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive review to the theory, application and research of machine learning for future wireless communications In one single volume, Machine Learning for Future Wireless Communications provides a comprehensive and highly accessible treatment to the theory, applications and current research developments to the technology aspects related to machine learning for wireless communications and networks. The technology development of machine learning for wireless communications has grown explosively and is one of the biggest trends in related academic, research and industry communities. Deep neural networks-based machine learning technology is a promising tool to attack the big challenge in wireless communications and networks imposed by the increasing demands in terms of capacity, coverage, latency, efficiency flexibility, compatibility, quality of experience and silicon convergence. The author – a noted expert on the topic – covers a wide range of topics including system architecture and optimization, physical-layer and cross-layer processing, air interface and protocol design, beamforming and antenna configuration, network coding and slicing, cell acquisition and handover, scheduling and rate adaption, radio access control, smart proactive caching and adaptive resource allocations. Uniquely organized into three categories: Spectrum Intelligence, Transmission Intelligence and Network Intelligence, this important resource: Offers a comprehensive review of the theory, applications and current developments of machine learning for wireless communications and networks Covers a range of topics from architecture and optimization to adaptive resource allocations Reviews state-of-the-art machine learning based solutions for network coverage Includes an overview of the applications of machine learning algorithms in future wireless networks Explores flexible backhaul and front-haul, cross-layer optimization and coding, full-duplex radio, digital front-end (DFE) and radio-frequency (RF) processing Written for professional engineers, researchers, scientists, manufacturers, network operators, software developers and graduate students, Machine Learning for Future Wireless Communications presents in 21 chapters a comprehensive review of the topic authored by an expert in the field.

Algorithm-Centric Design of Reliable and Efficient Deep Learning Processing Systems

Author : Elbruz Ozen
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (138 download)

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Book Synopsis Algorithm-Centric Design of Reliable and Efficient Deep Learning Processing Systems by : Elbruz Ozen

Download or read book Algorithm-Centric Design of Reliable and Efficient Deep Learning Processing Systems written by Elbruz Ozen and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence techniques driven by deep learning have experienced significant advancements in the past decade. The usage of deep learning methods has increased dramatically in practical application domains such as autonomous driving, healthcare, and robotics, where the utmost hardware resource efficiency, as well as strict hardware safety and reliability requirements, are often imposed. The increasing computational cost of deep learning models has been traditionally tackled through model compression and domain-specific accelerator design. As the cost of conventional fault tolerance methods is often prohibitive in consumer electronics, the question of functional safety and reliability for deep learning hardware is still in its infancy. This dissertation outlines a novel approach to deliver dramatic boosts in hardware safety, reliability, and resource efficiency through a synergistic co-design paradigm. We first observe and make use of the unique algorithmic characteristics of deep neural networks, including plasticity in the design process, resiliency to small numerical perturbations, and their inherent redundancy, as well as the unique micro-architectural properties of deep learning accelerators such as regularity. The advocated approach is accomplished by reshaping deep neural networks, enhancing deep neural network accelerators strategically, prioritizing the overall functional correctness, and minimizing the associated costs through the statistical nature of deep neural networks. To illustrate, our analysis demonstrates that deep neural networks equipped with the proposed techniques can maintain accuracy gracefully, even at extreme rates of hardware errors. As a result, the described methodology can embed strong safety and reliability characteristics in mission-critical deep learning applications at a negligible cost. The proposed approach further offers a promising avenue for handling the micro-architectural challenges of deep neural network accelerators and boosting resource efficiency through the synergistic co-design of deep neural networks and hardware micro-architectures.

Resource-Efficient Medical Image Analysis

Author : Xinxing Xu
Publisher : Springer Nature
ISBN 13 : 3031168763
Total Pages : 148 pages
Book Rating : 4.0/5 (311 download)

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Book Synopsis Resource-Efficient Medical Image Analysis by : Xinxing Xu

Download or read book Resource-Efficient Medical Image Analysis written by Xinxing Xu and published by Springer Nature. This book was released on 2022-09-15 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the first MICCAI Workshop on Resource-Efficient Medical Image Analysis, REMIA 2022, held in conjunction with MICCAI 2022, in September 2022 as a hybrid event. REMIA 2022 accepted 13 papers from the 19 submissions received. The workshop aims at creating a discussion on the issues for practical applications of medical imaging systems with data, label and hardware limitations.

Resource-Efficient Artificial Intelligence

Author : Nico Piatkowski
Publisher : de Gruyter
ISBN 13 : 9783110721058
Total Pages : 360 pages
Book Rating : 4.7/5 (21 download)

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Book Synopsis Resource-Efficient Artificial Intelligence by : Nico Piatkowski

Download or read book Resource-Efficient Artificial Intelligence written by Nico Piatkowski and published by de Gruyter. This book was released on 2022-09-22 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: For all autonomous devices the development of resource-aware machine learning techniques is required to reduce the tremendous resource consumption. This work provides theoretical and practical building blocks to bring full-fledged machine learning pipelines to systems with very low computational power or highly restricted energy supply. The presentation of theoretical methods is accompanied by actual learning results on ultra-low-power hardware.

Handbook on Federated Learning

Author : Saravanan Krishnan
Publisher : CRC Press
ISBN 13 : 1003837522
Total Pages : 381 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Handbook on Federated Learning by : Saravanan Krishnan

Download or read book Handbook on Federated Learning written by Saravanan Krishnan and published by CRC Press. This book was released on 2024-01-09 with total page 381 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mobile, wearable, and self-driving telephones are just a few examples of modern distributed networks that generate enormous amount of information every day. Due to the growing computing capacity of these devices as well as concerns over the transfer of private information, it has become important to process the part of the data locally by moving the learning methods and computing to the border of devices. Federated learning has developed as a model of education in these situations. Federated learning (FL) is an expert form of decentralized machine learning (ML). It is essential in areas like privacy, large-scale machine education and distribution. It is also based on the current stage of ICT and new hardware technology and is the next generation of artificial intelligence (AI). In FL, central ML model is built with all the data available in a centralised environment in the traditional machine learning. It works without problems when the predictions can be served by a central server. Users require fast responses in mobile computing, but the model processing happens at the sight of the server, thus taking too long. The model can be placed in the end-user device, but continuous learning is a challenge to overcome, as models are programmed in a complete dataset and the end-user device lacks access to the entire data package. Another challenge with traditional machine learning is that user data is aggregated at a central location where it violates local privacy policies laws and make the data more vulnerable to data violation. This book provides a comprehensive approach in federated learning for various aspects.

Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications

Author : Management Association, Information Resources
Publisher : IGI Global
ISBN 13 : 1799804151
Total Pages : 1671 pages
Book Rating : 4.7/5 (998 download)

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Book Synopsis Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications by : Management Association, Information Resources

Download or read book Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications written by Management Association, Information Resources and published by IGI Global. This book was released on 2019-10-11 with total page 1671 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to the growing use of web applications and communication devices, the use of data has increased throughout various industries. It is necessary to develop new techniques for managing data in order to ensure adequate usage. Deep learning, a subset of artificial intelligence and machine learning, has been recognized in various real-world applications such as computer vision, image processing, and pattern recognition. The deep learning approach has opened new opportunities that can make such real-life applications and tasks easier and more efficient. Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications is a vital reference source that trends in data analytics and potential technologies that will facilitate insight in various domains of science, industry, business, and consumer applications. It also explores the latest concepts, algorithms, and techniques of deep learning and data mining and analysis. Highlighting a range of topics such as natural language processing, predictive analytics, and deep neural networks, this multi-volume book is ideally designed for computer engineers, software developers, IT professionals, academicians, researchers, and upper-level students seeking current research on the latest trends in the field of deep learning.

Resource-Efficient Vehicle-to-Cloud Communications Leveraging Machine Learning

Author : Benjamin Sliwa
Publisher :
ISBN 13 : 9783844083569
Total Pages : 0 pages
Book Rating : 4.0/5 (835 download)

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Book Synopsis Resource-Efficient Vehicle-to-Cloud Communications Leveraging Machine Learning by : Benjamin Sliwa

Download or read book Resource-Efficient Vehicle-to-Cloud Communications Leveraging Machine Learning written by Benjamin Sliwa and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Deep Learning Illustrated

Author : Jon Krohn
Publisher : Addison-Wesley Professional
ISBN 13 : 0135121728
Total Pages : 725 pages
Book Rating : 4.1/5 (351 download)

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Book Synopsis Deep Learning Illustrated by : Jon Krohn

Download or read book Deep Learning Illustrated written by Jon Krohn and published by Addison-Wesley Professional. This book was released on 2019-08-05 with total page 725 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The authors’ clear visual style provides a comprehensive look at what’s currently possible with artificial neural networks as well as a glimpse of the magic that’s to come." – Tim Urban, author of Wait But Why Fully Practical, Insightful Guide to Modern Deep Learning Deep learning is transforming software, facilitating powerful new artificial intelligence capabilities, and driving unprecedented algorithm performance. Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline’s techniques. Packed with full-color figures and easy-to-follow code, it sweeps away the complexity of building deep learning models, making the subject approachable and fun to learn. World-class instructor and practitioner Jon Krohn–with visionary content from Grant Beyleveld and beautiful illustrations by Aglaé Bassens–presents straightforward analogies to explain what deep learning is, why it has become so popular, and how it relates to other machine learning approaches. Krohn has created a practical reference and tutorial for developers, data scientists, researchers, analysts, and students who want to start applying it. He illuminates theory with hands-on Python code in accompanying Jupyter notebooks. To help you progress quickly, he focuses on the versatile deep learning library Keras to nimbly construct efficient TensorFlow models; PyTorch, the leading alternative library, is also covered. You’ll gain a pragmatic understanding of all major deep learning approaches and their uses in applications ranging from machine vision and natural language processing to image generation and game-playing algorithms. Discover what makes deep learning systems unique, and the implications for practitioners Explore new tools that make deep learning models easier to build, use, and improve Master essential theory: artificial neurons, training, optimization, convolutional nets, recurrent nets, generative adversarial networks (GANs), deep reinforcement learning, and more Walk through building interactive deep learning applications, and move forward with your own artificial intelligence projects Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Resource-Efficient Methods in Machine Learning

Author : Kiran Nagesh Vodrahalli
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (134 download)

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Book Synopsis Resource-Efficient Methods in Machine Learning by : Kiran Nagesh Vodrahalli

Download or read book Resource-Efficient Methods in Machine Learning written by Kiran Nagesh Vodrahalli and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: To achieve our theoretical results, we often make use of the theory of Hermite polynomials -- an orthogonal function basis over the Gaussian measure. In the last chapter, we consider resource limitations in an online streaming setting. In particular, we consider how many data points from an oblivious adversarial stream we must store from one pass over the stream to output an additive approximation to the Support Vector Machine (SVM) objective, and prove stronger lower bounds on the memory complexity.

Deep Learning Applications, Volume 4

Author : M. Arif Wani
Publisher : Springer Nature
ISBN 13 : 9811961530
Total Pages : 394 pages
Book Rating : 4.8/5 (119 download)

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Book Synopsis Deep Learning Applications, Volume 4 by : M. Arif Wani

Download or read book Deep Learning Applications, Volume 4 written by M. Arif Wani and published by Springer Nature. This book was released on 2022-11-25 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a compilation of extended versions of selected papers from 20th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2021). It focuses on deep learning networks and their applications in domains such as healthcare, security and threat detection, fault diagnosis and accident analysis, and robotic control in industrial environments. It highlights novel ways of using deep neural networks to solve real-world problems, and also offers insights into deep learning architectures and algorithms, making it an essential reference guide for academic researchers, professionals, software engineers in industry, and innovative product developers. The book is fourth in the series published since 2017.

Resource and Data Efficient Deep Learning

Author : Cody Austun Coleman
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (126 download)

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Book Synopsis Resource and Data Efficient Deep Learning by : Cody Austun Coleman

Download or read book Resource and Data Efficient Deep Learning written by Cody Austun Coleman and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Using massive computation, deep learning allows machines to translate large amounts of data into models that accurately predict the real world, enabling powerful applications like virtual assistants and autonomous vehicles. As datasets and computer systems have continued to grow in scale, so has the quality of machine learning models, creating an expensive appetite in practitioners and researchers for data and computation. To address this demand, this dissertation discusses ways to measure and improve both the computational and data efficiency of deep learning. First, we introduce DAWNBench and MLPerf as a systematic way to measure end-to-end machine learning system performance. Researchers have proposed numerous hardware, software, and algorithmic optimizations to improve the computational efficiency of deep learning. While some of these optimizations perform the same operations faster (e.g., increasing GPU clock speed), many others modify the semantics of the training procedure (e.g., reduced precision) and can even impact the final model's accuracy on unseen data. Because of these trade-offs between accuracy and computational efficiency, it has been difficult to compare and understand the impact of these optimizations. We propose and evaluate a new metric, time-to-accuracy, that can be used to compare different system designs and use it to evaluate high performing systems by organizing two public benchmark competitions, DAWNBench and MLPerf. MLPerf has now grown into an industry standard benchmark co-organized by over 70 organizations. Second, we present ways to perform data selection on large-scale datasets efficiently. Data selection methods, such as active learning and core-set selection, improve the data efficiency of machine learning by identifying the most informative data points to label or train on. Across the data selection literature, there are many ways to identify these training examples. However, classical data selection methods are prohibitively expensive to apply in deep learning because of the larger datasets and models. To make these methods tractable, we propose (1) "selection via proxy" (SVP) to avoid expensive training and reduce the computation per example and (2) "similarity search for efficient active learning and search" (SEALS) to reduce the number of examples processed. Both methods lead to order of magnitude performance improvements, making techniques like active learning on billions of unlabeled images practical for the first time.

Monitoring Progress towards a Resource-Efficient and Circular Economy

Author : OECD
Publisher : OECD Publishing
ISBN 13 : 9264636889
Total Pages : 86 pages
Book Rating : 4.2/5 (646 download)

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Book Synopsis Monitoring Progress towards a Resource-Efficient and Circular Economy by : OECD

Download or read book Monitoring Progress towards a Resource-Efficient and Circular Economy written by OECD and published by OECD Publishing. This book was released on 2024-06-26 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt: Policies that foster the transition towards a more circular economy are gaining significant traction. Such policies are essential for a sustainable, low-carbon, resource-efficient and competitive economy. These developments bring about demands for reliable information to track progress and gauge results as well as for indicators that speak to policymakers and the public at large. This report presents a conceptual framework and indicator set to monitor progress and inform circular economy policies. It is designed to support OECD work on circular economy and provides a source of inspiration for countries seeking to build a coherent circular economy monitoring framework.