Download or read book Towards Safe and Efficient Application of Deep Neural Networks in Resource constrained Real time Embedded Systems written by Siyu Luan and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Efficient Implementation of Deep Neural Networks on Resource constrained Devices written by Maedeh Hemmat and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, Deep Neural Networks (DNNs) have emerged as an impressively successful model to perform complicated tasks including object classification, speech recognition, autonomous vehicle, etc. To provide better accuracy, state-of-the-art neural network models are designed to be deeper (i.e., having more layers) and larger (i.e., having more parameters within each layer). It subsequently has increased the computational and memory costs of DNNs, mandating their efficient hardware implementation, especially on resource-constrained devices such as embedded systems and mobile devices. This challenge can be investigated from two aspects: computation and storage. On one hand, state-of-the-art DNNs require the execution of billions of operations for each inference. This is while the computational power of embedded systems is tightly limited. On the other hand, DNN models require storage of several Megabytes of parameters which can't fit in the on-chip memory of these devices. More importantly, these systems are usually battery-powered with a limited energy budget to access memory and perform computations.This dissertation aims to make contributions towards improving the efficiency of DNN deployments on resource-constraint devices. Our contributions can be categorized into three aspects. First, we propose an iterative framework that enables dynamic reconfiguration of an already-trained Convolutional Neural Network (CNN) in hardware during inference. The reconfiguration enables input-dependent approximation of the CNN at run-time, leading to significant energy savings without any significant degradation in classification accuracy. Our proposed framework breaks each inference into several iterations and fetches only a fraction of the weights from off-chip memory at each iteration to perform the computations. It then decides to either terminate the network or fetch more weights to do the inference, based on the difficulty of the received input. The termination condition can be also adjusted to trade off classification accuracy and energy consumption at run-time. Second, we exploit the user-dependent behavior of DNNs and propose a personalized inference framework that prunes an already-trained neural network model based on the preferences of individual users and without the need to retrain the network. Our key observation is that an individual user may only encounter a tiny fraction of the trained classes on a regular basis. Hence, storing trained models (pruned or not) for all possible classes on local devices is costly and unnecessary for the user's needs. Our personalized framework minimizes the memory, computation, and energy consumption of the network on the local device as it processes neurons on a need basis (i.e., only when the user expects to encounter a specific output class). Third, we propose a framework for distributed inference of DNNs across multiple edge devices to improve the communication and latency overheads. Our framework utilizes many parallel, independent-running edge devices which communicate only once to a single 'back-end' device (also an edge device) to aggregate their predictions and produce the result of the inference. To achieve this distributed implementation, our framework first partitions the classes of the complex DNN into subsets to be assigned across the available edge devices while considering the computational resources of each device. The DNN is then aggressively pruned for each device for its set of assigned classes. Each smaller DNN (SNN) is further configured to return a 'Don't Know' when encountered by an input from an unassigned class. Each SNN is generated from the complex DNN at the beginning and then loaded onto its corresponding edge device, without the need for retraining. To perform inference, each SNN will perform an inference based on its received input.

Download or read book Embedded Machine Learning for Cyber Physical IoT and Edge Computing written by Sudeep Pasricha and published by Springer Nature. This book was released on 2023-11-07 with total page 571 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents recent advances towards the goal of enabling efficient implementation of machine learning models on resource-constrained systems, covering different application domains. The focus is on presenting interesting and new use cases of applying machine learning to innovative application domains, exploring the efficient hardware design of efficient machine learning accelerators, memory optimization techniques, illustrating model compression and neural architecture search techniques for energy-efficient and fast execution on resource-constrained hardware platforms, and understanding hardware-software codesign techniques for achieving even greater energy, reliability, and performance benefits. Discusses efficient implementation of machine learning in embedded, CPS, IoT, and edge computing; Offers comprehensive coverage of hardware design, software design, and hardware/software co-design and co-optimization; Describes real applications to demonstrate how embedded, CPS, IoT, and edge applications benefit from machine learning.

Download or read book Towards Deployment of Deep Neural Networks on Resource constrained Embedded Systems written by Boyu Zhang and published by . This book was released on 2019 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Neural Network (DNNs) have emerged as an important computational structure that facilitate important tasks such as speech and image recognition, autonomous vehicles, etc. In order to achieve better performance, such as higher classification accuracy, modern DNN models are designed to be more complex in terms of network structure and larger in terms of number of weights in the model. This imposes a great challenge for realizing DNN models on computation devices, especially those resource-constrained devices such as embedded and mobile systems. The challenge arises from three aspects: computation, memory, and energy consumption. First, the number of computations per inference required by modern large and complex DNN models is huge, whereas the computation capability available in the given systems may not be as powerful as a modern GPU or a dedicated processing unit. So, accomplishing the required computation within certain latency is an open challenge. Second, the conflict between the limited on-board memory resource and the static/run-time memory requirement of large DNN models also need to be resolved. Third, the very energy-consuming inference process places a heavy burden on edge devices' battery life. Since the majority of the total energy is consumed by data movement, the goal is not only to fit the DNN model into the system but also to optimize off-chip memory access in order to minimize energy consumption during inference. This dissertation aims to make contributions towards efficient realizations of DNN models on resource-constrained systems. Our contributions can be categorized into three aspects. First, we propose a structure simplification procedure that can identify and eliminate redundant neurons in any layer of a trained DNN model. Once the redundant neurons are identified and removed, the corresponding edges connected to those neurons will be eliminated as well. Then the new weight matrix is calculated directly by our procedure, while retraining may be applied to further recover the lost accuracy if necessary. We also propose a high-level energy model to better explore the tradeoffs in the design space during neuron elimination. Since both the neurons and their edges are eliminated, the memory and energy requirements are also get alleviated. Furthermore, the procedure also allows exploring the tradeoff between model performance and implementation cost. Second, since the convolutional layer is the most energy-consuming and computation heavy layer in Convolutional Neural Networks (CNNs), we propose a structural pruning technique to prune the input channels in convolutional layers. Once the redundant channels are identified and removed, the corresponding convolutional filters will be pruned as well. There significant reduction in static/run-time memory, computation, and energy consumption can be achieved. Moreover, the resulting pruned model is more efficient in terms of network architecture rather than specific weight values, which makes the theoretical reductions of implementation cost much easier to be harvested by existing hardware and software. Third, instead of blindly sending data to cloud and relying on cloud to perform inference, we propose to utilize the computation power of IoT devices to accomplish deep learning tasks while achieving higher degree of customization and privacy level. Specifically, we propose to incorporate a small-sized local customized DNN model to work with a large-sized general DNN model by using a "Mixture of Experts" architecture. Therefore, with minimal implementation overhead, the customized data can be handled by the small-sized DNN to achieve better performance without compromising the performance on general data. Our experiments show that the MoE architecture outperforms popular alternatives such as fine-tuning, bagging, independent ensemble, and multiple choice learning.

Download or read book Embedded Machine Learning for Cyber Physical IoT and Edge Computing written by Sudeep Pasricha and published by Springer Nature. This book was released on 2023-10-09 with total page 481 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents recent advances towards the goal of enabling efficient implementation of machine learning models on resource-constrained systems, covering different application domains. The focus is on presenting interesting and new use cases of applying machine learning to innovative application domains, exploring the efficient hardware design of efficient machine learning accelerators, memory optimization techniques, illustrating model compression and neural architecture search techniques for energy-efficient and fast execution on resource-constrained hardware platforms, and understanding hardware-software codesign techniques for achieving even greater energy, reliability, and performance benefits. Discusses efficient implementation of machine learning in embedded, CPS, IoT, and edge computing; Offers comprehensive coverage of hardware design, software design, and hardware/software co-design and co-optimization; Describes real applications to demonstrate how embedded, CPS, IoT, and edge applications benefit from machine learning.

Download or read book Embedded Artificial Intelligence written by Ovidiu Vermesan and published by CRC Press. This book was released on 2023-05-05 with total page 143 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent technological developments in sensors, edge computing, connectivity, and artificial intelligence (AI) technologies have accelerated the integration of data analysis based on embedded AI capabilities into resource-constrained, energy-efficient hardware devices for processing information at the network edge. Embedded AI combines embedded machine learning (ML) and deep learning (DL) based on neural networks (NN) architectures such as convolutional NN (CNN), or spiking neural network (SNN) and algorithms on edge devices and implements edge computing capabilities that enable data processing and analysis without optimised connectivity and integration, allowing users to access data from various sources. Embedded AI efficiently implements edge computing and AI processes on resource-constrained devices to mitigate downtime and service latency, and it successfully merges AI processes as a pivotal component in edge computing and embedded system devices. Embedded AI also enables users to reduce costs, communication, and processing time by assembling data and by supporting user requirements without the need for continuous interaction with physical locations. This book provides an overview of the latest research results and activities in industrial embedded AI technologies and applications, based on close cooperation between three large-scale ECSEL JU projects, AI4DI, ANDANTE, and TEMPO. The book’s content targets researchers, designers, developers, academics, post-graduate students and practitioners seeking recent research on embedded AI. It combines the latest developments in embedded AI, addressing methodologies, tools, and techniques to offer insight into technological trends and their use across different industries.

Download or read book Embedded Machine Learning for Cyber Physical IoT and Edge Computing written by Sudeep Pasricha and published by Springer Nature. This book was released on 2023-11-01 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents recent advances towards the goal of enabling efficient implementation of machine learning models on resource-constrained systems, covering different application domains. The focus is on presenting interesting and new use cases of applying machine learning to innovative application domains, exploring the efficient hardware design of efficient machine learning accelerators, memory optimization techniques, illustrating model compression and neural architecture search techniques for energy-efficient and fast execution on resource-constrained hardware platforms, and understanding hardware-software codesign techniques for achieving even greater energy, reliability, and performance benefits.

Download or read book Resource Constrained Neural Architecture Design written by Yunyang Xiong and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep neural networks have been highly effective for a wide range of applications in computer vision, natural language processing, speech recognition, medical imaging, and biology. Large amounts of annotated data, dedicated deep learning computing hardware such as the NVIDIA GPU and Google TPU, and the innovative neural network architectures and algorithms have all contributed to rapid advances over the last decade. Despite the foregoing improvements, the ever-growing amount of compute and data resources needed for training neural networks (whose sizes are growing quickly) as well as a need for deploying these models on embedded devices call for designing deep neural networks under various types of resource constraints. For example, low latency and real-time response of deep neural networks can be critical for various applications. While the complexity of deep neural networks can be reduced by model compression, different applications with diverse resource constraints pose unique challenges for neural network architecture design. For instance, each type of device has its own hardware idiosyncrasies and requires different deep architectures to achieve the best accuracy-efficiency trade-off. Consequently, designing neural networks that are adaptive and scalable to applications with diverse resource requirements is not trivial. We need methods that are capable of addressing different application-specific challenges paying attention to: (1) problem type (e.g., classification, object detection, sentence prediction), (2) resource challenges (e.g., strict inference compute, memory, and latency constraint, limited training computational resources, small sample sizes in scientific/biomedical problems). In this dissertation, we describe algorithms that facilitate neural architecture design while effectively addressing application- and domain-specific resource challenges. For diverse application domains, we study neural architecture design strategies respecting different resource needs ranging from test time efficiency to training efficiency and sample efficiency. We show the effectiveness of these ideas for learning with smaller datasets as well as enabling the deployment of deep learning systems on embedded devices with limited computational resources which may enable reducing the environmental effects of using such models.

Download or read book Towards Efficient Inference and Improved Training Efficiency of Deep Neural Networks written by Ravi Shanker Raju (Ph.D.) and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, deep neural networks have surpassed human performance on image classification tasks and and speech recognition. While current models can reach state of the art performance on stand-alone benchmarks, deploying them on embedded systems that have real-time latency deadlines either cause them to fail these requirements or severely get degraded in performance to meet the stated specifications. This requires intelligent design of the network architecture in order to minimize the accuracy degradation while deployed on the edge. Similarly, deep learning often has a long turn-around time due to the volume of the experiments on different hyperparameters and consumes time and resources. This motivates a need for developing training strategies that allow researchers who do not have access to large computational resources to train large models without waiting for exorbitant training cycles to be completed. This dissertation addresses these concerns through data dependent pruning of deep learning computation. First, regarding inference, we propose an integration of two different conditional execution strategies we call FBS-pruned CondConv by noticing that if we use input-specific filters instead of standard convolutional filters, we can aggressively prune at higher rates and mitigate accuracy degradation for significant computation savings. Then, regarding long training times, we introduce our dynamic data pruning framework which takes ideas from active learning and reinforcement learning to dynamically select subsets of data to train the model. Finally, as opposed to pruning data and in the same spirit of reducing training time, we investigate the vision transformer and introduce a unique training method called PatchDrop (originally designed for robustness to occlusions on transformers [1]), which uses the self-supervised DINO [2] model to identify the salient patches in an image and train on the salient subsets of an image. These strategies/training methods take a step in a direction to make models more accessible to deploy on edge devices in an efficient inference context and reduces the barrier for the independent researcher to train deep learning models which would require immense computational resources, pushing towards the democratization of machine learning.

Download or read book Deploying Deep Neural Networks in Embedded Real time Systems written by Adam Page and published by . This book was released on 2016 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep neural networks have been shown to outperform prior state-of-the-art solutions that rely heavily on hand-engineered features coupled with simple classification techniques. In addition to achieving several orders of magnitude improvement, they offer a number of additional benefits such as the ability to perform end-to-end learning by performing both hierarchical feature abstraction and inference. Furthermore, their success continues to be demonstrated in a growing number of fields for a wide-range of applications, including computer vision, speech recognition, and model forecasting. As this area of machine learning matures, a major challenge that remains is the ability to efficiently deploy such deep networks in embedded, resource-bound settings that have strict power and area budgets. While GPUs have been shown to improve throughput and energy efficiency over traditional computing paradigms, they still impose significant power burden for such low-power embedded settings. In order to further reduce power while still achieving desired throughput and accuracy, classification-efficient networks are required in addition to optimal deployment onto embedded hardware.

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.

Download or read book Real Time Embedded Systems written by Jiacun Wang and published by John Wiley & Sons. This book was released on 2017-07-10 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: Offering comprehensive coverage of the convergence of real-time embedded systems scheduling, resource access control, software design and development, and high-level system modeling, analysis and verification Following an introductory overview, Dr. Wang delves into the specifics of hardware components, including processors, memory, I/O devices and architectures, communication structures, peripherals, and characteristics of real-time operating systems. Later chapters are dedicated to real-time task scheduling algorithms and resource access control policies, as well as priority-inversion control and deadlock avoidance. Concurrent system programming and POSIX programming for real-time systems are covered, as are finite state machines and Time Petri nets. Of special interest to software engineers will be the chapter devoted to model checking, in which the author discusses temporal logic and the NuSMV model checking tool, as well as a chapter treating real-time software design with UML. The final portion of the book explores practical issues of software reliability, aging, rejuvenation, security, safety, and power management. In addition, the book: Explains real-time embedded software modeling and design with finite state machines, Petri nets, and UML, and real-time constraints verification with the model checking tool, NuSMV Features real-world examples in finite state machines, model checking, real-time system design with UML, and more Covers embedded computer programing, designing for reliability, and designing for safety Explains how to make engineering trade-offs of power use and performance Investigates practical issues concerning software reliability, aging, rejuvenation, security, and power management Real-Time Embedded Systems is a valuable resource for those responsible for real-time and embedded software design, development, and management. It is also an excellent textbook for graduate courses in computer engineering, computer science, information technology, and software engineering on embedded and real-time software systems, and for undergraduate computer and software engineering courses.

Download or read book Real Time Embedded Systems written by Meikang Qiu and published by CRC Press. This book was released on 2011-06-01 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ubiquitous in today's consumer-driven society, embedded systems use microprocessors that are hidden in our everyday products and designed to perform specific tasks. Effective use of these embedded systems requires engineers to be proficient in all phases of this effort, from planning, design, and analysis to manufacturing and marketing.Taking a sys

Download or read book Adversarial Machine Learning written by Aneesh Sreevallabh Chivukula and published by Springer Nature. This book was released on 2023-03-06 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: A critical challenge in deep learning is the vulnerability of deep learning networks to security attacks from intelligent cyber adversaries. Even innocuous perturbations to the training data can be used to manipulate the behaviour of deep networks in unintended ways. In this book, we review the latest developments in adversarial attack technologies in computer vision; natural language processing; and cybersecurity with regard to multidimensional, textual and image data, sequence data, and temporal data. In turn, we assess the robustness properties of deep learning networks to produce a taxonomy of adversarial examples that characterises the security of learning systems using game theoretical adversarial deep learning algorithms. The state-of-the-art in adversarial perturbation-based privacy protection mechanisms is also reviewed. We propose new adversary types for game theoretical objectives in non-stationary computational learning environments. Proper quantification of the hypothesis set in the decision problems of our research leads to various functional problems, oracular problems, sampling tasks, and optimization problems. We also address the defence mechanisms currently available for deep learning models deployed in real-world environments. The learning theories used in these defence mechanisms concern data representations, feature manipulations, misclassifications costs, sensitivity landscapes, distributional robustness, and complexity classes of the adversarial deep learning algorithms and their applications. In closing, we propose future research directions in adversarial deep learning applications for resilient learning system design and review formalized learning assumptions concerning the attack surfaces and robustness characteristics of artificial intelligence applications so as to deconstruct the contemporary adversarial deep learning designs. Given its scope, the book will be of interest to Adversarial Machine Learning practitioners and Adversarial Artificial Intelligence researchers whose work involves the design and application of Adversarial Deep Learning.

Download or read book Predictable and Runtime Adaptable Network On Chip for Mixed critical Real time Systems written by Sebastian Tobuschat and published by Cuvillier. This book was released on 2019-03-07 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: The industry of safety-critical and dependable embedded systems calls for even cheaper, high performance platforms that allow flexibility and an efficient verification of safety and real-time requirements. In this sense, flexibility denotes the ability to (online) adapt a system to changes (e.g. changing environment, application dynamics, errors) and the reuse-ability for different use cases. To cope with the increasing complexity of interconnected functions and to reduce the cost and power consumption of the system, multicore systems are used to efficiently integrate different processing units in the same chip. Networks-on-chip (NoCs), as a modular interconnect, are used as a promising solution for such multiprocessor systems on chip (MPSoCs), due to their scalability and performance. Hence, future NoC designs must face the aforementioned challenges. For safety-critical systems, a major goal is the avoidance of hazards. For this, safety-critical systems are qualified or even certified to prove the correctness of the functioning under all possible cases. A predictable behavior of the NoC can help to ease the qualification process (e.g. formal analysis) of the system. To achieve the required predictability, designers have two classes of solutions: isolation (quality of service (QoS) mechanisms) and (formal) analysis. For mixed-criticality systems, isolation and analysis approaches must be combined to efficiently achieve the desired predictability. Isolation techniques are used to bound interference between different application classes. And analysis can then be applied verifying the real-time applications and sufficient isolation properties. Traditional NoC analysis and architecture concepts tackle only a subpart of the challenges-they focus on either performance or predictability. Existing, predictable NoCs are deemed too expensive and inflexible to host a variety of applications with opposing constraints. And state-of-the-art analyses neglect certain platform pro

Download or read book Real Time Systems written by Hermann Kopetz and published by Springer Nature. This book was released on 2022-09-22 with total page 411 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book is a comprehensive text for the design of safety critical, hard real-time embedded systems. It offers a splendid example for the balanced, integrated treatment of systems and software engineering, helping readers tackle the hardest problems of advanced real-time system design, such as determinism, compositionality, timing and fault management. This book is an essential reading for advanced undergraduates and graduate students in a wide range of disciplines impacted by embedded computing and software. Its conceptual clarity, the style of explanations and the examples make the abstract concepts accessible for a wide audience." Janos Sztipanovits, Director E. Bronson Ingram Distinguished Professor of Engineering Institute for Software Integrated Systems Vanderbilt University Real-Time Systems focuses on hard real-time systems, which are computing systems that must meet their temporal specification in all anticipated load and fault scenarios. The book stresses the system aspects of distributed real-time applications, treating the issues of real-time, distribution and fault-tolerance from an integral point of view. A unique cross-fertilization of ideas and concepts between the academic and industrial worlds has led to the inclusion of many insightful examples from industry to explain the fundamental scientific concepts in a real-world setting. Compared to the Second Edition, new developments in communication standards for time-sensitive networks, such as TSN and Time-Triggered Ethernet are addressed. Furthermore, this edition includes a new chapter on real-time aspects in cloud and fog computing. The book is written as a standard textbook for a high-level undergraduate or graduate course on real-time embedded systems or cyber-physical systems. Its practical approach to solving real-time problems, along with numerous summary exercises, makes it an excellent choice for researchers and practitioners alike.

Download or read book Computer Vision ECCV 2020 Workshops written by Adrien Bartoli and published by Springer Nature. This book was released on 2021-01-30 with total page 752 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 6-volume set, comprising the LNCS books 12535 until 12540, constitutes the refereed proceedings of 28 out of the 45 workshops held at the 16th European Conference on Computer Vision, ECCV 2020. The conference was planned to take place in Glasgow, UK, during August 23-28, 2020, but changed to a virtual format due to the COVID-19 pandemic. The 249 full papers, 18 short papers, and 21 further contributions included in the workshop proceedings were carefully reviewed and selected from a total of 467 submissions. The papers deal with diverse computer vision topics. Part V includes: The 16th Embedded Vision Workshop; Real-World Computer Vision from Inputs with Limited Quality (RLQ); The Bright and Dark Sides of Computer Vision: Challenges and Opportunities for Privacy and Security (CV-COPS 2020); The Visual Object Tracking Challenge Workshop (VOT 2020); and Video Turing Test: Toward Human-Level Video Story Understanding.