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 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 Multi task Deep Learning Models for Real time Deployment in Embedded Systems written by Miquel Martí I Rabadán and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Multi-task Learning (MTL) is proposed as a way to speed up Deep Learning models for applications in which multiple tasks need to be solved simultaneously, which is especially useful in embedded, real-time systems such as the ones found in autonomous cars or UAVs. Multi-task networks take advantage of sharing resources for reducing the total inference time, memory footprint and model size. MTL was intended as an approach to improving their generalization capability so it seems to be a win-win approach. MTL is applied to a Computer Vision problem in which both Object Detection and Semantic Segmentation are solved based on the Single Shot Multibox Detector and Fully Convolutional Networks with skip connections respectively, using a ResNet-50 as the base network. Multi-task models are trained for two different datasets, Pascal VOC, which is used to validate the decisions made, and a combination of datasets with Aerial View images captured from UAVs. The challenges that appear during the process of training multi-task networks are analysed. However, they finally hinder the capacity of some multi-task models to reach the performance of the best single-task ones trained without the limitations impossed by applying MTL. Nevertheless, the multi-task networks clearly benefit from sharing resources and are 1.6x faster, lighter and use less memory compared to deploying the single-task models in parallel, which turns essential when running them on a Jetson TX1 SoC as the parallel approach does not fit into memory.

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 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 A Transfer Learning Approach to Object Detection Acceleration for Embedded Applications written by Lauren M. Vance and published by . This book was released on 2021 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning solutions to computer vision tasks have revolutionized many industries in recent years, but embedded systems have too many restrictions to take advantage of current state-of-the-art configurations. Typical embedded processor hardware configurations must meet very low power and memory constraints to maintain small and lightweight packaging, and the architectures of the current best deep learning models are too computationally-intensive for these hardware configurations. Current research shows that convolutional neural networks (CNNs) can be deployed with a few architectural modifications on Field-Programmable Gate Arrays (FPGAs) resulting in minimal loss of accuracy, similar or decreased processing speeds, and lower power consumption when compared to general-purpose Central Processing Units (CPUs) and Graphics Processing Units (GPUs). This research contributes further to these findings with the FPGA implementation of a YOLOv4 object detection model that was developed with the use of transfer learning. The transfer-learned model uses the weights of a model pre-trained on the MS-COCO dataset as a starting point then fine-tunes only the output layers for detection on more specific objects of five classes. The model architecture was then modified slightly for compatibility with the FPGA hardware using techniques such as weight quantization and replacing unsupported activation layer types. The model was deployed on three different hardware setups (CPU, GPU, FPGA) for inference on a test set of 100 images. It was found that the FPGA was able to achieve real-time inference speeds of 33.77 frames-per-second, a speedup of 7.74 frames-per-second when compared to GPU deployment. The model also consumed 96% less power than a GPU configuration with only approximately 4% average loss in accuracy across all 5 classes. The results are even more striking when compared to CPU deployment, with 131.7-times speedup in inference throughput. CPUs have long since been outperformed by GPUs for deep learning applications but are used in most embedded systems. These results further illustrate the advantages of FPGAs for deep learning inference on embedded systems even when transfer learning is used for an efficient end-to-end deployment process. This work advances current state-of-the-art with the implementation of a YOLOv4 object detection model developed with transfer learning for FPGA deployment.

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 Quantized Neural Networks and Neuromorphic Computing for Embedded Systems written by Shiya Liu and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning techniques have made great success in areas such as computer vision, speech recognition and natural language processing. Those breakthroughs made by deep learning techniques are changing every aspect of our lives. However, deep learning techniques have not realized their full potential in embedded systems such as mobiles, vehicles etc. because the high performance of deep learning techniques comes at the cost of high computation resource and energy consumption. Therefore, it is very challenging to deploy deep learning models in embedded systems because such systems have very limited computation resources and power constraints. Extensive research on deploying deep learning techniques in embedded systems has been conducted and considerable progress has been made. In this book chapter, we are going to introduce two approaches. The first approach is model compression, which is one of the very popular approaches proposed in recent years. Another approach is neuromorphic computing, which is a novel computing system that mimicks the human brain.

Download or read book TinyML written by Pete Warden and published by O'Reilly Media. This book was released on 2019-12-16 with total page 504 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary. Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures Work with Arduino and ultra-low-power microcontrollers Learn the essentials of ML and how to train your own models Train models to understand audio, image, and accelerometer data Explore TensorFlow Lite for Microcontrollers, Google’s toolkit for TinyML Debug applications and provide safeguards for privacy and security Optimize latency, energy usage, and model and binary size

Download or read book Deep Learning Networks written by Jayakumar Singaram and published by Springer Nature. This book was released on 2023-12-03 with total page 173 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook presents multiple facets of design, development and deployment of deep learning networks for both students and industry practitioners. It introduces a deep learning tool set with deep learning concepts interwoven to enhance understanding. It also presents the design and technical aspects of programming along with a practical way to understand the relationships between programming and technology for a variety of applications. It offers a tutorial for the reader to learn wide-ranging conceptual modeling and programming tools that animate deep learning applications. The book is especially directed to students taking senior level undergraduate courses and to industry practitioners interested in learning about and applying deep learning methods to practical real-world problems.

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 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 Context Aware Design and Optimization of Embedded Deep Neural Network Architectures written by Jinhang Choi and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Neural Networks (DNNs) have been demonstrated as state-of-the-art solutions for complex intelligence problems. DNNs derive their cognitive power by learning from millions of training examples in either a supervised or unsupervised fashion, and a critical aspect of the DNN system design procedure is the collection of large annotated training datasets that exhibit high coverage of the problem space. However, the more powerful a DNN we design, the more massive a target systems computation/memory requirements become, leading to disruptive I/O traffic in DNN computation, which restricts hardware/software design optimization for training and inference tasks with cutting-edge networks. As such, DNN deployment is confined to specialized accelerators satisfying response time in a given power budget, for which we have to design energy-efficient system architectures mitigating excessive off-chip memory accesses within a reasonable accuracy trade-off. Consequently, it is a challenge to use them in real-time embedded systems because of their requirements for computation-intensive operations and high memory bandwidth.This dissertation addresses DNN properties from the perspective of microarchitectural principles as well as machine learning, where two important characteristics are introduced: layer-wise data locality and dimensionality. To not suffer from low resource utilization while realizing expected performance, the accelerator designs should increase data locality, and reduce data dimensionality. To this end, context-aware application-specific architectural designs are explored for different DNN feature patterns. Based on representational/numerical analysis of DNN layers, diverse techniques are proposed ranging from microarchitectural design to system-level design automation by rearranging data locality, approximating data dimensionality, and truncating computation costs while increasing data-level parallelism.This dissertation also proposes context-aware optimization of training frameworks by revisiting the hardware-oriented design automation process. In addition to the traditional evaluation criteria such as performance, energy, and reliability, intelligent system architectures pose challenges with accuracy depending on the training process. Similar to automatic test vector generation, progressive data synthesis jointly tries to stochastically maximize training coverage while minimizing the number of training and validation cycles utilizing insights from functional verification. Furthermore, a randomized deviation of the targeted data synthesis is substantiated to improve the design robustness. Proof-of-concept experiments demonstrate the techniques for redesigning DNNs in distributed intelligence systems.Overall, this dissertation presents a) hardware/software co-design of off-chip memory access reduction in DNN architectures, b) DNN feature reduction that is orthogonal to and compatible with many other existing hardware/software optimizations, and c) design automation approaches for context-aware learning frameworks. These contributions collectively offer insight to best practices for design trade-offs between performance and accuracy in embedded systems, where this dissertation points toward future directions for energy-efficient intelligent architecture design.

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 Computer Vision ECCV 2018 written by Vittorio Ferrari and published by Springer. This book was released on 2018-10-06 with total page 855 pages. Available in PDF, EPUB and Kindle. Book excerpt: The sixteen-volume set comprising the LNCS volumes 11205-11220 constitutes the refereed proceedings of the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.The 776 revised papers presented were carefully reviewed and selected from 2439 submissions. The papers are organized in topical sections on learning for vision; computational photography; human analysis; human sensing; stereo and reconstruction; optimization; matching and recognition; video attention; and poster sessions.

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 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.