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Photonic Deep Neural Network Accelerators For Scaling To The Next Generation Of High Performance Processing
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Book Synopsis Photonic Deep Neural Network Accelerators for Scaling to the Next Generation of High-performance Processing by : Kyle D. Shiflett
Download or read book Photonic Deep Neural Network Accelerators for Scaling to the Next Generation of High-performance Processing written by Kyle D. Shiflett and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Improvements from electronic processor and interconnect performance scaling are narrowing due to fundamental challenges faced at the device level. Compounding the issue, increasing demand for large, accurate deep neural network models has placed significant pressure on the current generation of processors. The slowing of Moore’s law and the breakdown of Dennard scaling leaves no room for innovative solutions in traditional digital architectures to meet this demand. To address these scaling issues, architectures have moved away from general-purpose computation towards fixed-function hardware accelerators to handle demanding computation. Although electronic accelerators alleviate some of the pressure of deep neural network workloads, they are still burdened by electronic device and interconnect scaling problems. There is potential to further scale computer architectures by utilizing emerging technology, such as photonics. The low-loss interconnects and energy-efficient modulators provided by photonics could help drive future performance scaling. This could innovate the next generation of high-bandwidth, bandwidth-dense interconnects, and high-speed, energy-efficient processors by taking advantage of the inherent parallelism of light. This dissertation investigates photonic architectures for communication and computation acceleration to meet the machine learning processing requirements of future systems. The benefits of photonics is explored for bit-level parallelism, data-level parallelism, and in-network computation. The research performed in this dissertation shows that photonics has the4 potential to enable the next generation of deep neural network application performance by improving energy-efficiency and reducing compute latency. The evaluations in this dissertation conclude that photonic accelerators can: (1) Reduce energy-delay product by 73.9% at the bit-level on convolutional neural network workloads; (2) Improve throughput by 110× and improve energy-delay product by 74× on convolutions neural network workloads by exploiting data-level parallelism; (3) Improve network utilization while giving a 3.6× speedup, and reducing energy-delay product by 9.3× by performing in-network computatio
Book Synopsis Photonic Reservoir Computing by : Daniel Brunner
Download or read book Photonic Reservoir Computing written by Daniel Brunner and published by Walter de Gruyter GmbH & Co KG. This book was released on 2019-07-08 with total page 391 pages. Available in PDF, EPUB and Kindle. Book excerpt: Photonics has long been considered an attractive substrate for next generation implementations of machine-learning concepts. Reservoir Computing tremendously facilitated the realization of recurrent neural networks in analogue hardware. This concept exploits the properties of complex nonlinear dynamical systems, giving rise to photonic reservoirs implemented by semiconductor lasers, telecommunication modulators and integrated photonic chips.
Book Synopsis Neuromorphic Photonics by : Paul R. Prucnal
Download or read book Neuromorphic Photonics written by Paul R. Prucnal and published by CRC Press. This book was released on 2017-05-08 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book sets out to build bridges between the domains of photonic device physics and neural networks, providing a comprehensive overview of the emerging field of "neuromorphic photonics." It includes a thorough discussion of evolution of neuromorphic photonics from the advent of fiber-optic neurons to today’s state-of-the-art integrated laser neurons, which are a current focus of international research. Neuromorphic Photonics explores candidate interconnection architectures and devices for integrated neuromorphic networks, along with key functionality such as learning. It is written at a level accessible to graduate students, while also intending to serve as a comprehensive reference for experts in the field.
Book Synopsis Data Orchestration in Deep Learning Accelerators by : Tushar Krishna
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.
Book Synopsis Efficient Processing of Deep Neural Networks by : Vivienne Sze
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.
Book Synopsis Architecture Design for Highly Flexible and Energy-efficient Deep Neural Network Accelerators by : Yu-Hsin Chen (Ph. D.)
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.
Book Synopsis An OpenCL Framework for Real-time Inference of Next-generation Convolutional Neural Networks on FPGAs by : Sachin Kumawat
Download or read book An OpenCL Framework for Real-time Inference of Next-generation Convolutional Neural Networks on FPGAs written by Sachin Kumawat and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern Convolutional Neural Networks (CNNs) consist of billions of multiplications and additions which require the use of parallel computing units such as GPUs, FPGAs and other DSP processors. Consequently, General Purpose GPU (GPGPU) computing has taken this field by storm. At the same time, there has been an increased interest in FPGA based acceleration of CNN inference. In this work, we present FICaffe, a framework for FPGA-based Inference with Caffe, which provides a complete automated generation and mapping of CNN accelerators on FPGAs. We target applications with critical latency requirements and design high processing efficiency accelerators for CNNs. The architecture is structured in a highly concurrent OpenCL library, which enables High Level Synthesis tools to effectively exploit data, task and pipeline parallelism. We propose a unified memory model, that drives exploration of optimal design by matching on-chip and off-chip memory bandwidths available on FPGA platforms. We also identify origins of all clock cycle stalls and overheads inherent to CNN acceleration designs and provide a detailed model to accurately predict the runtime latency with less than 4% error against on-board tests. Furthermore, with FICaffe we provide support for cross-network synthesis, such that it is possible to processes a variety of CNNs, with reasonable efficiency, without long re-compilation hours. FICaffe is integrated with the popular deep learning framework Caffe, and is deployable to a wide variety of CNNs. FICaffe's efficacy is shown by mapping to a 28nm Stratix V GXA7 chip, and both network specific and cross-network performance are reported for AlexNet, VGG, SqueezeNet and GoogLeNet. We show a processing efficiency of 95.8% for the widely-reported VGG benchmark, which outperforms prior work. FICaffe also achieves more than 2X speedup on Stratix V GXA7 compared with the best published results on this chip, to the best of our knowledge.
Book Synopsis Programmable Integrated Photonics by : José Capmany
Download or read book Programmable Integrated Photonics written by José Capmany and published by . This book was released on 2020-02-21 with total page 361 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides the first comprehensive, up-to-date and self-contained introduction to the emergent field of Programmable Integrated Photonics (PIP). It covers both theoretical and practical aspects, ranging from basic technologies and the building of photonic component blocks, to designalternatives and principles of complex programmable photonic circuits, their limiting factors, techniques for characterization and performance monitoring/control, and their salient applications both in the classical as well as in the quantum information fields. The book concentrates and focusesmainly on the distinctive features of programmable photonics, as compared to more traditional ASPIC approaches.After some years during which the Application Specific Photonic Integrated Circuit (ASPIC) paradigm completely dominated the field of integrated optics, there has been an increasing interest in PIP. The rising interest in PIP is justified by the surge in a number of emerging applications that callfor true flexibility and reconfigurability, as well as low-cost, compact, and low-power consuming devices.Programmable Integrated Photonics is a new paradigm that aims at designing common integrated optical hardware configurations, which by suitable programming, can implement a variety of functionalities. These in turn can be exploited as basic operations in many application fields. Programmabilityenables, by means of external control signals, both chip reconfiguration for multifunction operation, as well as chip stabilization against non-ideal operations due to fluctuations in environmental conditions and fabrication errors. Programming also allows for the activation of parts of the chip,which are not essential for the implementation of a given functionality, but can be of help in reducing noise levels through the diversion of undesired reflections.
Book Synopsis Design of High-performance and Energy-efficient Accelerators for Convolutional Neural Networks by : Mahmood Azhar Qureshi
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.
Book Synopsis FPGA Overlay Processor for Deep Neural Networks by : yunxuan Yu
Download or read book FPGA Overlay Processor for Deep Neural Networks written by yunxuan Yu and published by . This book was released on 2020 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: The rapid advancement of Artificial intelligence (AI) is making our everyday life easier with smart assistants, automatic medical analyzer, bank plagiarism checkers and traffic predictions, etc. Deep learning algorithms, especially deep convolutional neuron networks (DCNNs), achieve top performance on AI tasks, but suffers from dense computational requirements, which calls for hardware acceleration. In this thesis we propose several architectures including compilation flow for general DCNN acceleration using FPGA platform. Starting from late 2015 we began to design customized accelerators for popular DCNNs such as VGG and YOLOv2. We reformulate the convolution computation by flattening it to large-scale matrix multiplication between feature maps and convolution kernels, which can be computed as inner product. With this formulation, the accelerators across all layers can be unified to enhance resource sharing, and maximize utilization of computing resources. We also quantized the network into 8bit with negligible accuracy loss to reduce memory footprint and computation resources. Different parallelism optimization strategies are explored for different networks. The VGG16 accelerator achieved 1.15x throughput under 1.5x lower frequency compared with state-of-the art designs. The YOLOv2 accelerator was commercialized and employed for real-time subway X-ray auto-hazard detection. Based on the experience we gained through customized accelerator designing, we designed a RTL compiler as an end-to-end solution to automatically generate RTL design for given CNN network and FPGA platform, which greatly reduced the human effort in developing a specific network accelerator. The compiler applies analytical performance models to optimize parameters for modules based on a handwritten template library, such that the overall throughput is maximized. Several levels of parallelism for convolution are explored, including inter feature-map, intra-kernel-set, input/output channel, etc. We also optimize architectures for block RAM and input buffers to speed up data flow. We tested our compiler on several well-known CNNs including AlexNet and VGGNet for different FPGA platforms. The resulting AlexNet is 113.69 GOPS on Xilinx VCU095 and 177.44 GOPS on VC707, and VGGNet is 226 GOPS on VCU095 under 100MHZ. These are 1.3x, 2.1x and 1.2x better than the best reported FPGA accelerators at that time, respectively. However, network-specific accelerator requires regeneration of logic and physical implementation whenever network is updated. Moreover, it certainly cannot handle cascaded network applications that are widely employed in complex real-world scenarios. Therefore, we propose a domain-specific FPGA overlay processor, named OPU to accelerate a wide range of CNN networks without re-configuration of FPGA for switch or update of CNN networks. We define our domain-specific instruction architecture with optimized granularity to maintain high efficiency while gaining extra progammability. We also built hardware micro-architectures on FPGA to verify ISA efficiency, and a compiler flow for parsing, optimization ans instructuin generation. Experiments show that OPU can achieve an average of 91% run-time MAC efficiency (RME) among various popular networks. Moreover, for VGG and YOLO networks, OPU outperforms automatically compiled network-specific accelerators in the literature. In addition, OPU shows 5.35x better power efficiency compared with Titan Xp. For a case using cascaded CNN networks, OPU is 2.9x faster compared with edge computing GPU Jetson Tx2 with a similar amount of computing resources. Our OPU platform was employed in an automatic curbside parking charging system in real-world. Using OPU as base design, we extend different versions of OPU to handle the newly emerged DCNN architectures. We have Light-OPU for light-weight DCNNs acceleration, where we modified the OPU architecture to fit the memory bounded light-weight operations. Our instruction architecture considers the sharing of major computation engine between LW operations and conventional convolution operations. This improves the run-time resource efficiency and overall power efficiency. Our experiments on seven major LW-CNNs show that Light-OPU achieves 5.5x better latency and 3.0x higher power efficiency on average compared with edge GPU NVIDIA Jetson TX2. Moreover, we also have Uni-OPU for the efficient uniform hardware acceleration of different types of transposed convolutional (TCONV) networks as well as conventional convolution (CONV) networks. Extra stage in compiler would transform the computation of Zero-inserting based TCONV (Zero-TCONV), nearest-neighbor resizing based TCONV (NN-TCONV) and CONV layers into the same pattern. The compiler conducts the following optimizations: (1) Eliminating up to 98.4% of operations in TCONV by making use of the fixed pattern of TCONV upsampling; (2) Decomposing and reformulating TCONV and CONV processes into streaming paralleled vector multiplication with uniform address generation scheme and data flow pattern. Uni-OPU can reach throughput up to 2.35 TOPS for TCONV layer. We evaluate \textit{Uni-OPU} on a benchmark set composed of six TCONV networks from different application fields. Extensive experimental results indicate that Uni-OPU is able to gain 1.45x to 3.68x superior power efficiency compared with state-of-the-art Zero-TCONV accelerators. High acceleration performance is also achieved on NN-TCONV networks, whose acceleration have not been explored before. In summary, we observe 15.04x and 12.43x higher power efficiency on Zero-TCONV and NN-TCONV networks compared with Titan Xp GPU on average. To the best of our knowledge, we are the first in-depth study to completely unify the computation process of both Zero-TCONV, NN-TCONV and CONV layers. In summary, we have been working on FPGA acceleration for deep learning vision algorithms. Several hand-coded customized accelerator as well as an auto-compiler that generates RTL code for customized accelerator have been developed. An initial tool-chain for an FPGA based overlay processor was also finished, which can compile DCNN network configuration file from popular deep learning platforms and map to processor for acceleration.
Book Synopsis On-Chip Communication Architectures by : Sudeep Pasricha
Download or read book On-Chip Communication Architectures written by Sudeep Pasricha and published by Morgan Kaufmann. This book was released on 2010-07-28 with total page 541 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the past decade, system-on-chip (SoC) designs have evolved to address the ever increasing complexity of applications, fueled by the era of digital convergence. Improvements in process technology have effectively shrunk board-level components so they can be integrated on a single chip. New on-chip communication architectures have been designed to support all inter-component communication in a SoC design. These communication architecture fabrics have a critical impact on the power consumption, performance, cost and design cycle time of modern SoC designs. As application complexity strains the communication backbone of SoC designs, academic and industrial R&D efforts and dollars are increasingly focused on communication architecture design. On-Chip Communication Architecures is a comprehensive reference on concepts, research and trends in on-chip communication architecture design. It will provide readers with a comprehensive survey, not available elsewhere, of all current standards for on-chip communication architectures. A definitive guide to on-chip communication architectures, explaining key concepts, surveying research efforts and predicting future trends Detailed analysis of all popular standards for on-chip communication architectures Comprehensive survey of all research on communication architectures, covering a wide range of topics relevant to this area, spanning the past several years, and up to date with the most current research efforts Future trends that with have a significant impact on research and design of communication architectures over the next several years
Book Synopsis Computational Imaging by : Ayush Bhandari
Download or read book Computational Imaging written by Ayush Bhandari and published by MIT Press. This book was released on 2022-10-25 with total page 482 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive and up-to-date textbook and reference for computational imaging, which combines vision, graphics, signal processing, and optics. Computational imaging involves the joint design of imaging hardware and computer algorithms to create novel imaging systems with unprecedented capabilities. In recent years such capabilities include cameras that operate at a trillion frames per second, microscopes that can see small viruses long thought to be optically irresolvable, and telescopes that capture images of black holes. This text offers a comprehensive and up-to-date introduction to this rapidly growing field, a convergence of vision, graphics, signal processing, and optics. It can be used as an instructional resource for computer imaging courses and as a reference for professionals. It covers the fundamentals of the field, current research and applications, and light transport techniques. The text first presents an imaging toolkit, including optics, image sensors, and illumination, and a computational toolkit, introducing modeling, mathematical tools, model-based inversion, data-driven inversion techniques, and hybrid inversion techniques. It then examines different modalities of light, focusing on the plenoptic function, which describes degrees of freedom of a light ray. Finally, the text outlines light transport techniques, describing imaging systems that obtain micron-scale 3D shape or optimize for noise-free imaging, optical computing, and non-line-of-sight imaging. Throughout, it discusses the use of computational imaging methods in a range of application areas, including smart phone photography, autonomous driving, and medical imaging. End-of-chapter exercises help put the material in context.
Download or read book Microcavities written by Alexey Kavokin and published by OUP Oxford. This book was released on 2011-04-27 with total page 487 pages. Available in PDF, EPUB and Kindle. Book excerpt: Rapid development of microfabrication and assembly of nanostructures has opened up many opportunities to miniaturize structures that confine light, producing unusual and extremely interesting optical properties. This book addresses the large variety of optical phenomena taking place in confined solid state structures: microcavities. Realisations include planar and pillar microcavities, whispering gallery modes, and photonic crystals. The microcavities represent a unique laboratory for quantum optics and photonics. They exhibit a number of beautiful effects including lasing, superfluidity, superradiance, entanglement etc. Written by four practitioners strongly involved in experiments and theories of microcavities, it is addressed to any interested reader having a general physical background, but in particular to undergraduate and graduate students at physics faculties.
Book Synopsis Neural Network Methods in Natural Language Processing by : Yoav Goldberg
Download or read book Neural Network Methods in Natural Language Processing written by Yoav Goldberg and published by Morgan & Claypool Publishers. This book was released on 2017-04-17 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural networks are a family of powerful machine learning models and this book focuses on their application to natural language data. The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics of working with machine learning over language data, and the use of vector-based rather than symbolic representations for words. It also covers the computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary neural network software libraries. The second part of the book (Parts III and IV) introduces more specialized neural network architectures, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models. These architectures and techniques are the driving force behind state-of-the-art algorithms for machine translation, syntactic parsing, and many other applications. Finally, we also discuss tree-shaped networks, structured prediction, and the prospects of multi-task learning.
Author :Sergio R. Restaino Publisher :SPIE-International Society for Optical Engineering ISBN 13 :9781628418071 Total Pages :130 pages Book Rating :4.4/5 (18 download)
Book Synopsis Introduction to Liquid Crystals for Optical Design and Engineering by : Sergio R. Restaino
Download or read book Introduction to Liquid Crystals for Optical Design and Engineering written by Sergio R. Restaino and published by SPIE-International Society for Optical Engineering. This book was released on 2015-06 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: Devices based on liquid crystals have become the mainstay of display technology used in mobile devices, vehicles, computer systems, and almost any other opportunity for information display imaginable. The aim of this book is to provide the optics community a liquid crystals primer that focuses on the optical components made from these fascinating materials. The book provides a functional overview of liquid crystal devices, their history, and their applications so that readers are prepared for more advanced texts and can continue to grow their abilities in this field. While it is not meant to be a complete mathematical treatise on the basics and applications of liquid crystals, the book does fill in some of the technical gaps, in particular in the area of adaptive optics applications.
Download or read book NANO-CHIPS 2030 written by Boris Murmann and published by Springer Nature. This book was released on 2020-06-08 with total page 597 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, a global team of experts from academia, research institutes and industry presents their vision on how new nano-chip architectures will enable the performance and energy efficiency needed for AI-driven advancements in autonomous mobility, healthcare, and man-machine cooperation. Recent reviews of the status quo, as presented in CHIPS 2020 (Springer), have prompted the need for an urgent reassessment of opportunities in nanoelectronic information technology. As such, this book explores the foundations of a new era in nanoelectronics that will drive progress in intelligent chip systems for energy-efficient information technology, on-chip deep learning for data analytics, and quantum computing. Given its scope, this book provides a timely compendium that hopes to inspire and shape the future of nanoelectronics in the decades to come.
Author :National Academies of Sciences, Engineering, and Medicine Publisher :National Academies Press ISBN 13 :030947969X Total Pages :273 pages Book Rating :4.3/5 (94 download)
Book Synopsis Quantum Computing by : National Academies of Sciences, Engineering, and Medicine
Download or read book Quantum Computing written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2019-04-27 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantum mechanics, the subfield of physics that describes the behavior of very small (quantum) particles, provides the basis for a new paradigm of computing. First proposed in the 1980s as a way to improve computational modeling of quantum systems, the field of quantum computing has recently garnered significant attention due to progress in building small-scale devices. However, significant technical advances will be required before a large-scale, practical quantum computer can be achieved. Quantum Computing: Progress and Prospects provides an introduction to the field, including the unique characteristics and constraints of the technology, and assesses the feasibility and implications of creating a functional quantum computer capable of addressing real-world problems. This report considers hardware and software requirements, quantum algorithms, drivers of advances in quantum computing and quantum devices, benchmarks associated with relevant use cases, the time and resources required, and how to assess the probability of success.
