Download or read book Energy efficient Convolutional Neural Network Accelerators for Edge Intelligence written by Alessandro Aimar and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Design of High performance and Energy efficient Accelerators for Convolutional Neural Networks written by Mahmood Azhar Qureshi 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 (DNNs) have gained significant traction in artificial intelligence (AI) applications over the past decade owing to a drastic increase in their accuracy. This huge leap in accuracy, however, translates into a sizable model and high computational requirements, something which resource-limited mobile platforms struggle against. Embedding AI inference into various real-world applications requires the design of high-performance, area, and energy-efficient accelerator architectures. In this work, we address the problem of the inference accelerator design for dense and sparse convolutional neural networks (CNNs), a type of DNN which forms the backbone of modern vision-based AI systems. We first introduce a fully dense accelerator architecture referred to as the NeuroMAX accelerator. Most traditional dense CNN accelerators rely on single-core, linear processing elements (PEs), in conjunction with 1D dataflows, for accelerating the convolution operations in a CNN. This limits the maximum achievable ratio of peak throughput per PE count to unity. Most of the past works optimize their dataflows to attain close to 100% hardware utilization to reach this ratio. In the NeuroMAX accelerator, we design a high-throughput, multi-threaded, log-based PE core. The designed core provides a 200% increase in peak throughput per PE count while only incurring a 6% increase in the hardware area overhead compared to a single, linear multiplier PE core with the same output bit precision. NeuroMAX accelerator also uses a 2D weight broadcast dataflow which exploits the multi-threaded nature of the PE cores to achieve a high hardware utilization per layer for various dense CNN models. Sparse convolutional neural network models reduce the massive compute and memory bandwidth requirements inherently present in dense CNNs without a significant loss in accuracy. Designing sparse accelerators for the processing of sparse CNN models, however, is much more challenging compared to the design of dense CNN accelerators. The micro-architecture design, the design of sparse PEs, addressing the load-balancing issues, and the system-level architectural design issues for processing the entire sparse CNN model are some of the key technical challenges that need to be addressed in order to design a high-performance and energy-efficient sparse CNN accelerator architecture. We break this problem down into two parts. In the first part, using some of the concepts from the dense NeuroMAX accelerator, we introduce SparsePE, a multi-threaded, and flexible PE, capable of handling both the dense and sparse CNN model computations. The SparsePE core uses the binary mask representation to actively skip ineffective sparse computations involving zeros, and favors valid, non-zero computations, thereby, drastically increasing the effective throughput and the hardware utilization of the core as compared to a dense PE core. In the second part, we generate a two-dimensional (2D) mesh architecture of the SparsePE cores, which we refer to as the Phantom accelerator. We also propose a novel dataflow that supports processing of all layers of a CNN, including unit and non-unit stride convolutions (CONV), and fully-connected (FC) layers. In addition, the Phantom accelerator uses a two-level load balancing strategy to minimize the computational idling, thereby, further improving the hardware utilization, throughput, as well as the energy efficiency of the accelerator. The performance of the dense and the sparse accelerators is evaluated using a custom-built cycle accurate performance simulator and performance is compared against recent works. Logic utilization on hardware is also compared against the prior works. Finally, we conclude by mentioning some more techniques for accelerating CNNs and presenting some other avenues where the proposed work can be applied.

Download or read book Hardware Accelerator Systems for Artificial Intelligence and Machine Learning written by and published by Academic Press. This book was released on 2021-03-28 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Volume 122 delves into arti?cial Intelligence and the growth it has seen with the advent of Deep Neural Networks (DNNs) and Machine Learning. Updates in this release include chapters on Hardware accelerator systems for artificial intelligence and machine learning, Introduction to Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Deep Learning with GPUs, Edge Computing Optimization of Deep Learning Models for Specialized Tensor Processing Architectures, Architecture of NPU for DNN, Hardware Architecture for Convolutional Neural Network for Image Processing, FPGA based Neural Network Accelerators, and much more. Updates on new information on the architecture of GPU, NPU and DNN Discusses In-memory computing, Machine intelligence and Quantum computing Includes sections on Hardware Accelerator Systems to improve processing efficiency and performance

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 Energy efficient Accelerator SOC for Convolutional Neural Network Training written by 江子近 and published by . This book was released on 2019 with total page 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 Architecture Design of Energy efficient Reconfigurable Deep Convolutional Neural Network Accelerator written by 陳奕愷 and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Energy efficient ASIC Accelerators for Machine deep Learning Algorithms written by Minkyu Kim and published by . This book was released on 2019 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this work, to reduce computation without accuracy degradation, an energy-efficient deep convolutional neural network (DCNN) accelerator is proposed based on a novel conditional computing scheme and integrates convolution with subsequent max-pooling operations. This way, the total number of bit-wise convolutions could be reduced by ~2x, without affecting the output feature values. This work also has been developing an optimized dataflow that exploits sparsity, maximizes data re-use and minimizes off-chip memory access, which can improve upon existing hardware works. The total off-chip memory access can be saved by 2.12x. Preliminary results of the proposed DCNN accelerator achieved a peak 7.35 TOPS/W for VGG-16 by post-layout simulation results in 40nm. A number of recent efforts have attempted to design custom inference engine based on various approaches, including the systolic architecture, near memory processing, and in-meomry computing concept. This work evaluates a comprehensive comparison of these various approaches in a unified framework. This work also presents the proposed energy-efficient in-memory computing accelerator for deep neural networks (DNNs) by integrating many instances of in-memory computing macros with an ensemble of peripheral digital circuits, which supports configurable multibit activations and large-scale DNNs seamlessly while substantially improving the chip-level energy efficiency. Proposed accelerator is fully designed in 65nm, demonstrating ultralow

Download or read book All digital Time domain CNN Engine for Energy Efficient Edge Computing written by Shirin Fathima and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning is finding applications in a wide variety of areas ranging from autonomous cars to genomics. Machine learning tasks such as image classification, speech recognition and object detection are being used in most of the modern computing systems. In particular, Convolutional Neural Networks (CNNs, class of artificial neural networks) are extensively used for many such ML applications, due to their state of the art classification accuracy at a much lesser complexity compared to their fully connected network counterpart. However, the CNN inference process requires intensive compute and memory resources making it challenging to implement in energy constrained edge devices. The major operation of a CNN is the Multiplication and Accumulate (MAC) operation. These operations are traditionally performed by digital adders and multipliers, which dissipates large amount of power. In this 2-phase work, an energy efficient time-domain approach is used to perform the MAC operation using the concept of Memory Delay Line (MDL). Phase I of this work implements LeNet-5 CNN to classify MNIST dataset (handwritten digits) and is demonstrated on a commercial 40nm CMOS Test-chip. Phase II of this work aims to scale-up this work for multi-bit weights and implements AlexNet CNN to classify 1000-class ImageNet dataset images

Download or read book Architecture Design for Highly Flexible and Energy efficient Deep Neural Network Accelerators written by Yu-Hsin Chen (Ph. D.) and published by . This book was released on 2018 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep neural networks (DNNs) are the backbone of modern artificial intelligence (AI). However, due to their high computational complexity and diverse shapes and sizes, dedicated accelerators that can achieve high performance and energy efficiency across a wide range of DNNs are critical for enabling AI in real-world applications. To address this, we present Eyeriss, a co-design of software and hardware architecture for DNN processing that is optimized for performance, energy efficiency and flexibility. Eyeriss features a novel Row-Stationary (RS) dataflow to minimize data movement when processing a DNN, which is the bottleneck of both performance and energy efficiency. The RS dataflow supports highly-parallel processing while fully exploiting data reuse in a multi-level memory hierarchy to optimize for the overall system energy efficiency given any DNN shape and size. It achieves 1.4x to 2.5x higher energy efficiency than other existing dataflows. To support the RS dataflow, we present two versions of the Eyeriss architecture. Eyeriss v1 targets large DNNs that have plenty of data reuse. It features a flexible mapping strategy for high performance and a multicast on-chip network (NoC) for high data reuse, and further exploits data sparsity to reduce processing element (PE) power by 45% and off-chip bandwidth by up to 1.9x. Fabricated in a 65nm CMOS, Eyeriss v1 consumes 278 mW at 34.7 fps for the CONV layers of AlexNet, which is 10× more efficient than a mobile GPU. Eyeriss v2 addresses support for the emerging compact DNNs that introduce higher variation in data reuse. It features a RS+ dataflow that improves PE utilization, and a flexible and scalable NoC that adapts to the bandwidth requirement while also exploiting available data reuse. Together, they provide over 10× higher throughput than Eyeriss v1 at 256 PEs. Eyeriss v2 also exploits sparsity and SIMD for an additional 6× increase in throughput.

Download or read book Towards Energy Efficient Convolutional Neural Network Inference written by Lukas Arno Jakob Cavigelli and published by . This book was released on 2019 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Accelerators for Convolutional Neural Networks written by Arslan Munir and published by John Wiley & Sons. This book was released on 2023-10-16 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: Accelerators for Convolutional Neural Networks Comprehensive and thorough resource exploring different types of convolutional neural networks and complementary accelerators Accelerators for Convolutional Neural Networks provides basic deep learning knowledge and instructive content to build up convolutional neural network (CNN) accelerators for the Internet of things (IoT) and edge computing practitioners, elucidating compressive coding for CNNs, presenting a two-step lossless input feature maps compression method, discussing arithmetic coding -based lossless weights compression method and the design of an associated decoding method, describing contemporary sparse CNNs that consider sparsity in both weights and activation maps, and discussing hardware/software co-design and co-scheduling techniques that can lead to better optimization and utilization of the available hardware resources for CNN acceleration. The first part of the book provides an overview of CNNs along with the composition and parameters of different contemporary CNN models. Later chapters focus on compressive coding for CNNs and the design of dense CNN accelerators. The book also provides directions for future research and development for CNN accelerators. Other sample topics covered in Accelerators for Convolutional Neural Networks include: How to apply arithmetic coding and decoding with range scaling for lossless weight compression for 5-bit CNN weights to deploy CNNs in extremely resource-constrained systems State-of-the-art research surrounding dense CNN accelerators, which are mostly based on systolic arrays or parallel multiply-accumulate (MAC) arrays iMAC dense CNN accelerator, which combines image-to-column (im2col) and general matrix multiplication (GEMM) hardware acceleration Multi-threaded, low-cost, log-based processing element (PE) core, instances of which are stacked in a spatial grid to engender NeuroMAX dense accelerator Sparse-PE, a multi-threaded and flexible CNN PE core that exploits sparsity in both weights and activation maps, instances of which can be stacked in a spatial grid for engendering sparse CNN accelerators For researchers in AI, computer vision, computer architecture, and embedded systems, along with graduate and senior undergraduate students in related programs of study, Accelerators for Convolutional Neural Networks is an essential resource to understanding the many facets of the subject and relevant applications.

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 Towards Heterogeneous Multi core Systems on Chip for Edge Machine Learning written by Vikram Jain and published by Springer Nature. This book was released on 2023-09-15 with total page 199 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores and motivates the need for building homogeneous and heterogeneous multi-core systems for machine learning to enable flexibility and energy-efficiency. Coverage focuses on a key aspect of the challenges of (extreme-)edge-computing, i.e., design of energy-efficient and flexible hardware architectures, and hardware-software co-optimization strategies to enable early design space exploration of hardware architectures. The authors investigate possible design solutions for building single-core specialized hardware accelerators for machine learning and motivates the need for building homogeneous and heterogeneous multi-core systems to enable flexibility and energy-efficiency. The advantages of scaling to heterogeneous multi-core systems are shown through the implementation of multiple test chips and architectural optimizations.

Download or read book An Adaptive Framework for Energy Efficient Edge AI written by Kannappan Jayakodi Nitthilan and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A large number of real-time artificial intelligence (AI) applications including robotics, self-driving cars, smart health and augmented (AR) / virtual reality (VR) are enhanced/boosted by deploying deep neural networks (DNNs). Currently, computation for most of these applications happens on the cloud due to huge compute, energy, and memory requirements. However, moving these applications to the edge platforms such as smartphones and AR/VR headsets reduce latency and improves user experience, accessibility, and data privacy. Existing solutions utilize high-performance and energy-efficient hardware accelerators and software solutions including sparsity and quantization of weights with a potential compromise on the prediction accuracy.This dissertation proposes an adaptive framework for energy-efficient edge AI which complements all the previous solutions enhancing the performance on edge devices. We utilize the intuitive idea that easy inputs require simple networks and hard inputs require complex networks. This framework is based on three key ideas. First, we design and train a space of DNNs of increasing complexity (coarse to fine). Second, we perform an input-specific adaptive inference by selecting a DNN of appropriate complexity depending on the hardness of input examples. Third, we execute the selected DNN on the target edge platform using a resource management policy to save energy. We demonstrate the generalization of the proposed solution for three qualitatively different problem settings ranging from convolutional neural networks (CNNs) for simple image classification to structured generative adversarial networks (GANs) for photo-realistic unconditional image generation and graph convolutional networks (GCNs) for 3D shape synthesis. Our experiments on real-world applications on edge platforms demonstrate a significant reduction in energy and latency with little to no loss in prediction accuracy.

Download or read book An Energy Efficient Accelerator with Relative Indexing Memory for Sparse Compressed Convolutional Neural Network written by and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Learning Based Techniques for Energy Efficient and Secure Computation on the Edge written by Jia Guo and published by . This book was released on 2019 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the paradigm of Internet-of-Things (IoT), smart devices will proliferate our living and working spaces. The recent decade has already witnessed an explosive growth of smartphones and wearable devices. A plethora of newer and even more powerful systems are emerging. IoT will enable more fluid human-computer interaction and immersive experiences in smart homes. IoT will facilitate rich sensing and actuating in intelligent warehousing and manufacturing. IoT will also empower fast and accurate perception and decision making in autonomous vehicles. The paradigm has elevated the role of the devices that constitute the edge of the network. Because of the sensitive nature and the sheer volume of the data generated by those devices, edge computing becomes a more effective and efficient option. While it brings better privacy protection and latency reduction in applications, edge computing is associated with various constraints. For the sizable list of devices that are operating on batteries, their sustainable operation usually calls for extremely efficient and judicious use of energy. Further, the inherent vulnerability accompanying the deployment in unsafe environments requires extra layers of security. In this dissertation, we study the energy and security problems of edge computing in the context of machine learning. We present various learning-based techniques for improving energy efficiency. In contrast to the traditional resource allocation mechanisms that typically adopt handcrafted rules and heuristics, we adopt a framework where we use machine learning learn to create online resource allocation strategies from optimal offline solutions. We demonstrate the effectiveness of the framework in applications and scenarios including DVFS, computation offloading and sensor networks. In the video decoding case, our machine learning enabled strategies have approximated optimal solutions with an average of 2\% error and achieved 40\% in energy savings. In an increasing number of edge computing applications, machine learning algorithms themselves constitute the core and the major workload. Many of those applications have high energy consumption and are vulnerable to security issues such as intellectual property theft. To solve the problems, we derive techniques directly from the machine learning processes. We present computer vision-oriented adaptive subsampling strategies for image sensors, model pruning and customization methods for deep neural networks, and deep neural network watermarking for intellectual property protection. These techniques improve energy efficiency and security of machine learning at very little or even zero cost of the performance of the models.