Download or read book Accelerators for Convolutional Neural Networks written by Arslan Munir and published by John Wiley & Sons. This book was released on 2023-10-31 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 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 Proceedings of the 40th Chinese Control Conference written by Cheng Peng 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 Hardware and Bandwidth Efficient Reconfigurable Accelerators for Deep Convolutional Neural Networks written by and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Data Orchestration in Deep Learning Accelerators written by Tushar Krishna and published by Springer Nature. This book was released on 2022-05-31 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Synthesis Lecture focuses on techniques for efficient data orchestration within DNN accelerators. The End of Moore's Law, coupled with the increasing growth in deep learning and other AI applications has led to the emergence of custom Deep Neural Network (DNN) accelerators for energy-efficient inference on edge devices. Modern DNNs have millions of hyper parameters and involve billions of computations; this necessitates extensive data movement from memory to on-chip processing engines. It is well known that the cost of data movement today surpasses the cost of the actual computation; therefore, DNN accelerators require careful orchestration of data across on-chip compute, network, and memory elements to minimize the number of accesses to external DRAM. The book covers DNN dataflows, data reuse, buffer hierarchies, networks-on-chip, and automated design-space exploration. It concludes with data orchestration challenges with compressed and sparse DNNs and future trends. The target audience is students, engineers, and researchers interested in designing high-performance and low-energy accelerators for DNN inference.

Download or read book Green Electronics written by Cristian Ravariu and published by BoD – Books on Demand. This book was released on 2018-06-20 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Green Electronics book is intended to stimulate people's thinking toward the new concepts of an environment-friendly electronics - the main challenge in the future. The book offers multiple solutions to push the classical electronic industry toward green concepts, aided by nanotechnologies, with revolutionary features that provide low power consumption in electronics, use biomaterials for integrated structures, and include environmental monitoring tools. Based on organic semiconductors/insulators without toxic precursors, green electronic technologies launched promising devices like OLED, OTFT, or nano-core-shell transistors. The Green Electronics book successfully presents the recent directions collected worldwide and leaves free space for continuing year by year with new subtopics.

Download or read book Approximate Circuits written by Sherief Reda and published by Springer. This book was released on 2018-12-17 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides readers with a comprehensive, state-of-the-art overview of approximate computing, enabling the design trade-off of accuracy for achieving better power/performance efficiencies, through the simplification of underlying computing resources. The authors describe in detail various efforts to generate approximate hardware systems, while still providing an overview of support techniques at other computing layers. The book is organized by techniques for various hardware components, from basic building blocks to general circuits and systems.

Download or read book 2019 IEEE Computer Society Annual Symposium on VLSI ISVLSI written by IEEE Staff and published by . This book was released on 2019-07-15 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This Symposium explores emerging trends and novel ideas and concepts in the area of VLSI The Symposium covers a range of topics from VLSI circuits, systems and design methods to system level design and system on chip issues, to bringing VLSI experience to new areas and technologies Future design methodologies as well as new CAD tools to support them will also be the key topics ISVLSI 2019 highlights a special theme of Neuromoprhic Computing Over almost two decades the Symposium has been a unique forum promoting multidisciplinary research and new visionary approaches in the area of VLSI

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 Advanced Computer Architecture written by Chao Li and published by Springer. This book was released on 2018-09-12 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 12th Annual Conference on Advanced Computer Architecture, ACA 2018, held in Yingkou, China, in August 2018. The 17 revised full papers presented were carefully reviewed and selected from 80 submissions. The papers of this volume are organized in topical sections on: accelerators; new design explorations; towards efficient ML/AI; parallel computing system.

Download or read book Convolution Neural Network Hardware Accelerator for Handwritten Digital Classification written by Afwan Khan and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This project aims to develop and test a Hardware Accelerator for a Convolutional Neural Network capable of analyzing handwritten digits. Convolutional neural networks can be defined as neural networks which make use of perceptrons for supervised learning. Image processing, natural language processing, and other cognitive tasks can be handled by CNNs. Hardware acceleration refers to the process of shifting certain computations from the general-purpose CPU to specialized components within the system, increasing the efficiency of the system beyond what is possible using software running on a general-purpose CPU alone. In general, if an application is running on a purely general-purpose CPU, certain computations are performed more efficiently than if it used a hardware accelerator. As part of this project, the model for a classifying system is designed using LeNet-based CNNs. The MNIST dataset is used to train the Python model, and then the hardware implementation is done with Xilinx Vivado Design Suite. The model is then tested on images provided by users. The aim is to use the Zynq Z7 FPGA to implement the classifier system and decrease the processing time required.

Download or read book VLSI Design and Test written by S. Rajaram and published by Springer. This book was released on 2019-01-24 with total page 722 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 22st International Symposium on VLSI Design and Test, VDAT 2018, held in Madurai, India, in June 2018. The 39 full papers and 11 short papers presented together with 8 poster papers were carefully reviewed and selected from 231 submissions. The papers are organized in topical sections named: digital design; analog and mixed signal design; hardware security; micro bio-fluidics; VLSI testing; analog circuits and devices; network-on-chip; memory; quantum computing and NoC; sensors and interfaces.

Download or read book Kernel Unfolding Approach Over Nvm Crossbar Accelerators for Convolutional Neural Networks written by 巫岳翰 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 Artificial Intelligence and Hardware Accelerators written by Ashutosh Mishra and published by Springer Nature. This book was released on 2023-03-15 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores new methods, architectures, tools, and algorithms for Artificial Intelligence Hardware Accelerators. The authors have structured the material to simplify readers’ journey toward understanding the aspects of designing hardware accelerators, complex AI algorithms, and their computational requirements, along with the multifaceted applications. Coverage focuses broadly on the hardware aspects of training, inference, mobile devices, and autonomous vehicles (AVs) based AI accelerators

Download or read book Design of Hardware Accelerators for Hierarchical Temporal Memory and Convolutional Neural Network written by Weifu Li and published by . This book was released on 2019 with total page 92 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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: