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 Deep Learning written by Bert Moons and published by Springer. This book was released on 2018-10-23 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
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 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 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 Efficient Design of Scalable Deep Neural Networks for Resource Constrained Edge Devices written by Mohammad Loni and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 0 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 IoT enabled Convolutional Neural Networks Techniques and Applications written by Mohd Naved and published by CRC Press. This book was released on 2023-05-08 with total page 409 pages. Available in PDF, EPUB and Kindle. Book excerpt: Convolutional neural networks (CNNs), a type of deep neural network that has become dominant in a variety of computer vision tasks, in recent years, CNNs have attracted interest across a variety of domains due to their high efficiency at extracting meaningful information from visual imagery. CNNs excel at a wide range of machine learning and deep learning tasks. As sensor-enabled internet of things (IoT) devices pervade every aspect of modern life, it is becoming increasingly critical to run CNN inference, a computationally intensive application, on resource-constrained devices. Through this edited volume, we aim to provide a structured presentation of CNN-enabled IoT applications in vision, speech, and natural language processing. This book discusses a variety of CNN techniques and applications, including but not limited to, IoT enabled CNN for speech denoising, a smart app for visually impaired people, disease detection, ECG signal analysis, weather monitoring, texture analysis, etc. Unlike other books on the market, this book covers the tools, techniques, and challenges associated with the implementation of CNN algorithms, computation time, and the complexity associated with reasoning and modelling various types of data. We have included CNNs' current research trends and future directions.
Download or read book IoT Fundamentals written by David Hanes and published by Cisco Press. This book was released on 2017-05-30 with total page 782 pages. Available in PDF, EPUB and Kindle. Book excerpt: Today, billions of devices are Internet-connected, IoT standards and protocols are stabilizing, and technical professionals must increasingly solve real problems with IoT technologies. Now, five leading Cisco IoT experts present the first comprehensive, practical reference for making IoT work. IoT Fundamentals brings together knowledge previously available only in white papers, standards documents, and other hard-to-find sources—or nowhere at all. The authors begin with a high-level overview of IoT and introduce key concepts needed to successfully design IoT solutions. Next, they walk through each key technology, protocol, and technical building block that combine into complete IoT solutions. Building on these essentials, they present several detailed use cases, including manufacturing, energy, utilities, smart+connected cities, transportation, mining, and public safety. Whatever your role or existing infrastructure, you’ll gain deep insight what IoT applications can do, and what it takes to deliver them. Fully covers the principles and components of next-generation wireless networks built with Cisco IOT solutions such as IEEE 802.11 (Wi-Fi), IEEE 802.15.4-2015 (Mesh), and LoRaWAN Brings together real-world tips, insights, and best practices for designing and implementing next-generation wireless networks Presents start-to-finish configuration examples for common deployment scenarios Reflects the extensive first-hand experience of Cisco experts
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 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 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 Deploying Deep Neural Networks with Resource Constraints written by Theresa VanderWeide and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep neural networks (DNNs) have recently gained unprecedented success in various domains. In resource-constrained edge systems (e.g., mobile devices and IoT devices), QoS-aware DNNs are required to meet latency and memory/storage requirements of mission-critical deep learning applications. There is a growing need to deploy deep learning on resource constrained devices. In this thesis, we propose two solutions to this issue: BlinkNet, which is a runtime system that can guarantee both latency and memory/storage bounds for one or multiple DNNs via efficient QoS-aware per-layer approximation. And ParamExplorer, which evaluates hyperparameters of DNNs converted to Spiking Neural Networks (SNNs) and their effect on accuracy in comparison to the original DNN. ParamExplorer evaluates the search space and identifies an optimal hyperparameter configuration to reduce loss of accuracy.
Download or read book Towards Efficient Implementation of Neuromorphic Systems with Emerging Device Technologies written by Farnood Merrikh Bayat and published by . This book was released on 2015 with total page 157 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nowadays with unbounded expansion of digital world, powerful information processing systems governed by deep learning algorithms are becoming more and more popular. In this situation, usage of fast, powerful, intelligent and trainable deep learning methods seems critical and unavoidable. However, despite of their inherent structural and conceptual differences, all of these intelligent methods and systems share one common property i.e. having enormous number of trainable parameters. However, from a hardware point of view, the size of a practical computing system is always determined based on available resources. In this dissertation, we study these deep learning methods from a hardware point of view and investigate the possibility of their hardware implementation based on two new emerging technologies i.e. resistive switching and floating gate (flash) devices. For this purpose, memristive devices are fabricated with high density in crossbar structure to create a network which then trained with modified RPROB rule to successfully classify images. In addition, biologically plausible spike-timing dependent plasticity rule and its dependence to initial state is demonstrated experimentally on these nano-scale devices. Similar procedure is followed for the other technology, i.e. flash devices. We modified and fabricated the conventional design of digital flash memories which provide us with the ability of individual programming of floating-gate transistors. Having large-scale neural networks in mind, an efficient and high speed tuning method is developed based on acquired dynamic and static models which are then tested experimentally on commercial devices. We have also experimentally investigated the possibility of implementing vector-to-matrix multiplier using these devices which is the main building block of most deep learning methods. Finally, a multi-layer neural network is designed and fabricated using this technology to classify handwritten digits.
Download or read book Co designing Model Compression Algorithms and Hardware Accelerators for Efficient Deep Learning written by Ritchie Zhao and published by . This book was released on 2020 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the past decade, machine learning (ML) with deep neural networks (DNNs) has become extremely successful in a variety of application domains including computer vision, natural language processing, and game AI. DNNs are now a primary topic of academic research among computer scientists, and a key component of commercial technologies such as web search, recommendation systems, and self-driving vehicles. However, factors such as the growing complexity of DNN models, the diminished benefits of technology scaling, and the proliferation of resource-constrained edge devices are driving a demand for higher DNN performance and energy efficiency. Consequently, neural network training and inference have begun to shift from commodity general-purpose processors (e.g., CPUs and GPUs) to custom-built hardware accelerators (e.g., FPGAs and ASICs). In line with this trend, there has been extensive research on specialized algorithms and architectures for dedicated DNN processors. Furthermore, the rapid pace of innovation in DNN algorithm space is mismatched with the time-consuming process of hardware implementation. This has generated increased interest in novel design methodologies and tools which can reduce the human effort and turn-around time of hardware design. This thesis studies how low-precision quantization and structured matrices can improve the performance and energy efficiency of DNNs running on specialized accelerators. We co-design both the DNN compression algorithms and the accelerator architectures, enabling us to evaluate the impact of our ideas on real hardware. In the process, we examine the use of high-level synthesis tools in reducing the hardware design effort. This thesis represents a cross-domain research effort at efficient deep learning. First, we propose specialized architectures for accelerating binarized neural networks on FPGA. Second, we study novel high-level synthesis techniques to reduce the manual effort in FPGA accelerator design. Third, we show a fundamental link between group convolutions and circulant matrices, two previously disparate lines of research in DNN compression. Using this insight we propose HadaNet, an alternative to circulant compression which achieve identical accuracy with asymptotically fewer multiplications. Fourth, we present outlier channel splitting, a technique to improve DNN weight quantization by removing outliers from the weight distribution without arduous retraining. Finally, we show preliminary results on overwrite quantization, a technique which address outliers in DNN activation quantization using extremely lightweight architectural extensions to a spatial accelerator template.
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 Designing Efficient Machine Learning Architectures for Edge Devices written by Tianen Chen and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning has proliferated on many Internet-of-Things (IoT) applications designed for edge devices. Energy efficiency is one of the most crucial constraints in the design of machine learning applications on IoT devices due to battery and energy-harvesting power sources. Previous attempts use the cloud to transmit data back and forth onto the edge device to alleviate energy strain, but this comes at a great latency and privacy cost. Approximate computing has emerged as a promising solution to bypass the cloud by reducing the energy cost of secure computation ondevice while maintaining high accuracy and low latency. Within machine learning, approximate computing can be used on overparameterized deep neural networks (DNNs) by removing the redundancy by sparsifying the network connections. This thesis attempts to leverage approximate computing techniques on the hardware and software-side of DNNs in order to port onto edge devices with limited power supplies. This thesis aims to implement reconfigurable approximate computing on low-power edge devices, allowing for optimization of the energy-quality tradeoff depending on application specifics. These objectives are achieved by three tasks as follows: i) hardware-side memory-aware logic synthesization, ii) designing energy-aware model compression techniques, and, iii) optimizing edge offloading techniques for efficient client and server communication. These contributions will help facilitate the efficient implementation of edge machine learning on resource-constrained embedded systems.
