Architecture Design For Highly Flexible And Energy Efficient Deep Neural Network Accelerators

Download Architecture Design For Highly Flexible And Energy Efficient Deep Neural Network Accelerators full books in PDF, epub, and Kindle. Read online Architecture Design For Highly Flexible And Energy Efficient Deep Neural Network Accelerators ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!

Architecture Design for Highly Flexible and Energy-efficient Deep Neural Network Accelerators

Author : Yu-Hsin Chen (Ph. D.)
Publisher :
ISBN 13 :
Total Pages : 147 pages
Book Rating : 4.:/5 (15 download)

DOWNLOAD NOW!

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.

Efficient Processing of Deep Neural Networks

Author : Vivienne Sze
Publisher : Springer Nature
ISBN 13 : 3031017668
Total Pages : 254 pages
Book Rating : 4.0/5 (31 download)

DOWNLOAD NOW!

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.

Design Space Exploration and Architecture Design for Inference and Training Deep Neural Networks

Author : Yangjie Qi
Publisher :
ISBN 13 :
Total Pages : 187 pages
Book Rating : 4.:/5 (13 download)

DOWNLOAD NOW!

Book Synopsis Design Space Exploration and Architecture Design for Inference and Training Deep Neural Networks by : Yangjie Qi

Download or read book Design Space Exploration and Architecture Design for Inference and Training Deep Neural Networks written by Yangjie Qi and published by . This book was released on 2021 with total page 187 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Neural Networks (DNNs) are widely used in various application domains and achieve remarkable results. However, DNNs require a large number of computations for both the inference and training phases. Hardware accelerators are designed and implemented to compute DNN models efficiently. Many accelerators have been proposed for DNN inference, while only a limited set of DNN training accelerators has been proposed. Almost all of these accelerators are highly custom-designed and limited in the types of networks they can process. This dissertation focuses on designing novel architectures and tools for efficient training of deep neural networks, particularly for edge applications. We proposed several novel architectures and a design space exploration tool. Our proposed architecture can be used for efficient processing of DNNs, and the design space exploration model could help DNN architects explore the design space of DNN architecture design for both inference and training and help home in on the optimal architecture in different hardware constraints in applications. The first area of contribution in this dissertation is the design of Socrates-D-1, a digital multicore on-chip learning architecture for deep neural networks. This processing unit design demonstrates the capability to process the training phase of DNNs efficiently. A statically time-multiplexed routing mechanism and a co-designed mapping method are also introduced to improve overall throughput and energy efficiency. The experimental results show 6.8 to 22.3 times speedup and more than a thousand times energy efficiency over a GPGPU. The proposed architecture is also compared with several DNN training accelerators and achieves the best energy and area efficiencies. The second area of contribution in this dissertation is the design of Socrates-D-2, which is an enhanced version of Socrates-D-1. This architecture presents a novel neural processing unit design. A dual-ported eDRAM memory replaces the double eDRAM memory design used in Socrates-D-1. In addition, a new mapping method utilizing neural network pruning techniques is introduced and evaluated with several datasets. The co-designed mapping methods helped the architecture achieve both throughput and energy efficiency without loss of accuracy. Compared with Socrates-D-1, this new architecture shows an average of 1.2 times higher energy efficiency and 1.25 times better area efficiency. The third area of contribution in this dissertation is the development of TRIM, a design space exploration model for DNN accelerators. TRIM is an infrastructure model and can explore the design space of DNN accelerators for training and inference. It utilizes a very flexible hardware template, which can model a wide range of architectures. TRIM explores the design space of data partition and reuse strategies for each hardware architecture and estimates the optimal time and energy. Our experimental results show that TRIM can achieve more than eighty percent accuracy on time and energy estimations. To the best of our knowledge, TRIM is the first infrastructure to model and explore the design space of DNN accelerators for training and inference. The fourth area of contribution in this dissertation is a set of design space explorations using TRIM. Through several case studies, we explored the design space of DNN accelerators for training and inference. We compared different dataflows and showed the impact of dataflow on efficient processing DNNs. We showed how to use TRIM to optimize the dataflow. We explored the design space of spatial architectures and showed the results of varying different hardware choices. Based on the exploration results, several high throughput and energy-efficient DNN training accelerators were presented. The fifth area of contribution in this dissertation is the design of an FPGA-based training accelerator for edge devices. We designed a CPU-FPGA accelerator that can operate under 5W. TRIM is utilized for dataflow optimization and hardware parameter selection. The experimental results show that we could achieve a 1.93 times speedup and 1.43 times energy efficiency for end-to-end training over a CPU implementation.

Accelerator Architecture for Secure and Energy Efficient Machine Learning

Author : Mohammad Hossein Samavatian
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (139 download)

DOWNLOAD NOW!

Book Synopsis Accelerator Architecture for Secure and Energy Efficient Machine Learning by : Mohammad Hossein Samavatian

Download or read book Accelerator Architecture for Secure and Energy Efficient Machine Learning written by Mohammad Hossein Samavatian and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: ML applications are driving the next computing revolution. In this context both performance and security are crucial. We propose hardware/software co-design solutions for addressing both. First, we propose RNNFast, an accelerator for Recurrent Neural Networks (RNNs). RNNs are particularly well suited for machine learning problems in which context is important, such as language translation. RNNFast leverages an emerging class of non-volatile memory called domain-wall memory (DWM). We show that DWM is very well suited for RNN acceleration due to its very high density and low read/write energy. RNNFast is very efficient and highly scalable, with a flexible mapping of logical neurons to RNN hardware blocks. The accelerator is designed to minimize data movement by closely interleaving DWM storage and computation. We compare our design with a state-of-the-art GPGPU and find 21.8X higher performance with 70X lower energy. Second, we brought ML security into ML accelerator design for more efficiency and robustness. Deep Neural Networks (DNNs) are employed in an increasing number of applications, some of which are safety-critical. Unfortunately, DNNs are known to be vulnerable to so-called adversarial attacks. In general, the proposed defenses have high overhead, some require attack-specific re-training of the model or careful tuning to adapt to different attacks. We show that these approaches, while successful for a range of inputs, are insufficient to address stronger, high-confidence adversarial attacks. To address this, we propose HASI and DNNShield, two hardware-accelerated defenses that adapt the strength of the response to the confidence of the adversarial input. Both techniques rely on approximation or random noise deliberately introduced into the model. HASI uses direct noise injection into the model at inference. DNNShield uses approximation that relies on dynamic and random sparsification of the DNN model to achieve inference approximation efficiently and with fine-grain control over the approximation error. Both techniques use the output distribution characteristics of noisy/sparsified inference compared to a baseline output to detect adversarial inputs. We show an adversarial detection rate of 86% when applied to VGG16 and 88% when applied to ResNet50, which exceeds the detection rate of the state-of-the-art approaches, with a much lower overhead. We demonstrate a software/hardware-accelerated FPGA prototype, which reduces the performance impact of HASI and DNNShield relative to software-only CPU and GPU implementations.

Design of High-performance and Energy-efficient Accelerators for Convolutional Neural Networks

Author : Mahmood Azhar Qureshi
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (133 download)

DOWNLOAD NOW!

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.

Data Orchestration in Deep Learning Accelerators

Author : Tushar Krishna
Publisher : Springer Nature
ISBN 13 : 3031017676
Total Pages : 158 pages
Book Rating : 4.0/5 (31 download)

DOWNLOAD NOW!

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.

Architecture Design of Energy-efficient Reconfigurable Deep Convolutional Neural Network Accelerator

Author : 陳奕愷
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (17 download)

DOWNLOAD NOW!

Book Synopsis Architecture Design of Energy-efficient Reconfigurable Deep Convolutional Neural Network Accelerator by : 陳奕愷

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:

Energy-efficient Accelerator Architecture for Neural Network Training and Its Circuit Design

Author : 莊宗翰
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (18 download)

DOWNLOAD NOW!

Book Synopsis Energy-efficient Accelerator Architecture for Neural Network Training and Its Circuit Design by : 莊宗翰

Download or read book Energy-efficient Accelerator Architecture for Neural Network Training and Its Circuit Design written by 莊宗翰 and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Deep Learning for Computer Architects

Author : Brandon Reagen
Publisher : Morgan & Claypool Publishers
ISBN 13 : 1627059857
Total Pages : 125 pages
Book Rating : 4.6/5 (27 download)

DOWNLOAD NOW!

Book Synopsis Deep Learning for Computer Architects by : Brandon Reagen

Download or read book Deep Learning for Computer Architects written by Brandon Reagen and published by Morgan & Claypool Publishers. This book was released on 2017-08-22 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a primer written for computer architects in the new and rapidly evolving field of deep learning. It reviews how machine learning has evolved since its inception in the 1960s and tracks the key developments leading up to the emergence of the powerful deep learning techniques that emerged in the last decade. Machine learning, and specifically deep learning, has been hugely disruptive in many fields of computer science. The success of deep learning techniques in solving notoriously difficult classification and regression problems has resulted in their rapid adoption in solving real-world problems. The emergence of deep learning is widely attributed to a virtuous cycle whereby fundamental advancements in training deeper models were enabled by the availability of massive datasets and high-performance computer hardware. It also reviews representative workloads, including the most commonly used datasets and seminal networks across a variety of domains. In addition to discussing the workloads themselves, it also details the most popular deep learning tools and show how aspiring practitioners can use the tools with the workloads to characterize and optimize DNNs. The remainder of the book is dedicated to the design and optimization of hardware and architectures for machine learning. As high-performance hardware was so instrumental in the success of machine learning becoming a practical solution, this chapter recounts a variety of optimizations proposed recently to further improve future designs. Finally, it presents a review of recent research published in the area as well as a taxonomy to help readers understand how various contributions fall in context.

Design and Performance Analysis of Hardware Accelerator for Deep Neural Network in Heterogeneous Platform

Author : Md Syadus Sefat
Publisher :
ISBN 13 :
Total Pages : 196 pages
Book Rating : 4.:/5 (18 download)

DOWNLOAD NOW!

Book Synopsis Design and Performance Analysis of Hardware Accelerator for Deep Neural Network in Heterogeneous Platform by : Md Syadus Sefat

Download or read book Design and Performance Analysis of Hardware Accelerator for Deep Neural Network in Heterogeneous Platform written by Md Syadus Sefat and published by . This book was released on 2018 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis describes a new flexible approach to implementing energy-efficient DNN accelerator on FPGAs. Our design leverages the Coherent Accelerator Processor Interface (CAPI) which provides a cache-coherent view of system memory to attached accelerators. Computational kernels are accelerated on a CAPI-supported Kintex FPGA board. Our implementation bypasses the need for device driver code and significantly reduces the communication and I/O transfer overhead. To improve the performance of the entire application, we propose a collaborative model of execution in which the control of the data flow within the accelerator is kept independent, freeing-up CPU cores to work on other parts of the application. For further performance enhancements, we propose a technique to exploit data locality in the cache, situated in the CAPI Power Service Layer (PSL). Finally, we develop a resource-conscious implementation for more efficient utilization of resources and improved scalability. Compared with the previous work, our architecture achieves both improved performance and better power efficiency.

Towards Heterogeneous Multi-core Systems-on-Chip for Edge Machine Learning

Author : Vikram Jain
Publisher : Springer Nature
ISBN 13 : 3031382307
Total Pages : 199 pages
Book Rating : 4.0/5 (313 download)

DOWNLOAD NOW!

Book Synopsis Towards Heterogeneous Multi-core Systems-on-Chip for Edge Machine Learning by : Vikram Jain

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.

Mixed-precision NN Accelerator with Neural-hardware Architecture Search

Author : Yujun Lin (S. M.)
Publisher :
ISBN 13 :
Total Pages : 65 pages
Book Rating : 4.:/5 (119 download)

DOWNLOAD NOW!

Book Synopsis Mixed-precision NN Accelerator with Neural-hardware Architecture Search by : Yujun Lin (S. M.)

Download or read book Mixed-precision NN Accelerator with Neural-hardware Architecture Search written by Yujun Lin (S. M.) and published by . This book was released on 2020 with total page 65 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural architecture and hardware architecture co-design is an effective way to enable specialization and acceleration for deep neural networks (DNNs). The design space and its exploration methodology impact efficiency and productivity. However, both architecture designs are challenging. We first propose a mixed-precision accelerator, a highly parameterized architecture that can adapt to different bit widths for different quantized layers with significantly reduced overhead. It efficiently provides a vast design space for both neural and hardware architecture. However, it is difficult to exhaust such an enormous design space by rule-based heuristics. To tackle this problem, we propose a machine learning based design and optimization methodology of a neural network accelerator. It includes the evolution strategy based hardware architecture search and one-shot HyperNet based quantized neural architecture search. Evaluated on existing DNN benchmarks, our mixed-precision accelerator achieves 11.7x, 1.5x speedup and 10.5x, 1.9x energy savings over Eyeriss [3] and BitFusion [35] respectively under the same area, frequency, and process technology. Our machine learning based co-design can compose highly matched neural-hardware architectures and further rival the best human-designed architectures by additional 1.3x speedup and 1.5x energy savings under the same ImageNet accuracy with better sample efficiency.

Co-designing Model Compression Algorithms and Hardware Accelerators for Efficient Deep Learning

Author : Ritchie Zhao
Publisher :
ISBN 13 :
Total Pages : 130 pages
Book Rating : 4.:/5 (124 download)

DOWNLOAD NOW!

Book Synopsis Co-designing Model Compression Algorithms and Hardware Accelerators for Efficient Deep Learning by : Ritchie Zhao

Download or read book Co-designing Model Compression Algorithms and Hardware Accelerators for Efficient Deep Learning written by Ritchie Zhao and published by . This book was released on 2020 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the past decade, machine learning (ML) with deep neural networks (DNNs) has become extremely successful in a variety of application domains including computer vision, natural language processing, and game AI. DNNs are now a primary topic of academic research among computer scientists, and a key component of commercial technologies such as web search, recommendation systems, and self-driving vehicles. However, factors such as the growing complexity of DNN models, the diminished benefits of technology scaling, and the proliferation of resource-constrained edge devices are driving a demand for higher DNN performance and energy efficiency. Consequently, neural network training and inference have begun to shift from commodity general-purpose processors (e.g., CPUs and GPUs) to custom-built hardware accelerators (e.g., FPGAs and ASICs). In line with this trend, there has been extensive research on specialized algorithms and architectures for dedicated DNN processors. Furthermore, the rapid pace of innovation in DNN algorithm space is mismatched with the time-consuming process of hardware implementation. This has generated increased interest in novel design methodologies and tools which can reduce the human effort and turn-around time of hardware design. This thesis studies how low-precision quantization and structured matrices can improve the performance and energy efficiency of DNNs running on specialized accelerators. We co-design both the DNN compression algorithms and the accelerator architectures, enabling us to evaluate the impact of our ideas on real hardware. In the process, we examine the use of high-level synthesis tools in reducing the hardware design effort. This thesis represents a cross-domain research effort at efficient deep learning. First, we propose specialized architectures for accelerating binarized neural networks on FPGA. Second, we study novel high-level synthesis techniques to reduce the manual effort in FPGA accelerator design. Third, we show a fundamental link between group convolutions and circulant matrices, two previously disparate lines of research in DNN compression. Using this insight we propose HadaNet, an alternative to circulant compression which achieve identical accuracy with asymptotically fewer multiplications. Fourth, we present outlier channel splitting, a technique to improve DNN weight quantization by removing outliers from the weight distribution without arduous retraining. Finally, we show preliminary results on overwrite quantization, a technique which address outliers in DNN activation quantization using extremely lightweight architectural extensions to a spatial accelerator template.

Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design

Author : Nan Zheng
Publisher : John Wiley & Sons
ISBN 13 : 1119507405
Total Pages : 389 pages
Book Rating : 4.1/5 (195 download)

DOWNLOAD NOW!

Book Synopsis Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design by : Nan Zheng

Download or read book Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design written by Nan Zheng and published by John Wiley & Sons. This book was released on 2019-10-18 with total page 389 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explains current co-design and co-optimization methodologies for building hardware neural networks and algorithms for machine learning applications This book focuses on how to build energy-efficient hardware for neural networks with learning capabilities—and provides co-design and co-optimization methodologies for building hardware neural networks that can learn. Presenting a complete picture from high-level algorithm to low-level implementation details, Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design also covers many fundamentals and essentials in neural networks (e.g., deep learning), as well as hardware implementation of neural networks. The book begins with an overview of neural networks. It then discusses algorithms for utilizing and training rate-based artificial neural networks. Next comes an introduction to various options for executing neural networks, ranging from general-purpose processors to specialized hardware, from digital accelerator to analog accelerator. A design example on building energy-efficient accelerator for adaptive dynamic programming with neural networks is also presented. An examination of fundamental concepts and popular learning algorithms for spiking neural networks follows that, along with a look at the hardware for spiking neural networks. Then comes a chapter offering readers three design examples (two of which are based on conventional CMOS, and one on emerging nanotechnology) to implement the learning algorithm found in the previous chapter. The book concludes with an outlook on the future of neural network hardware. Includes cross-layer survey of hardware accelerators for neuromorphic algorithms Covers the co-design of architecture and algorithms with emerging devices for much-improved computing efficiency Focuses on the co-design of algorithms and hardware, which is especially critical for using emerging devices, such as traditional memristors or diffusive memristors, for neuromorphic computing Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design is an ideal resource for researchers, scientists, software engineers, and hardware engineers dealing with the ever-increasing requirement on power consumption and response time. It is also excellent for teaching and training undergraduate and graduate students about the latest generation neural networks with powerful learning capabilities.

ECML PKDD 2018 Workshops

Author : Anna Monreale
Publisher : Springer
ISBN 13 : 3030148807
Total Pages : 127 pages
Book Rating : 4.0/5 (31 download)

DOWNLOAD NOW!

Book Synopsis ECML PKDD 2018 Workshops by : Anna Monreale

Download or read book ECML PKDD 2018 Workshops written by Anna Monreale and published by Springer. This book was released on 2019-03-07 with total page 127 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes revised selected papers from the workshops DMLE and IoTStream, held at the 18thEuropean Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2018, in Dublin, Ireland, in September 2018. The 8 full papers presented in this volume were carefully reviewed and selected from a total of 12 submissions. The workshops included are: DMLE 2018: First Workshop on Decentralized Machine Learning at the Edge IoTStream 2018: 3rd Workshop on IoT Large Scale Machine Learning from Data Streams

Energy-efficient Deep Learning Accelerator

Author : Jingyang Zhu
Publisher :
ISBN 13 :
Total Pages : 136 pages
Book Rating : 4.:/5 (18 download)

DOWNLOAD NOW!

Book Synopsis Energy-efficient Deep Learning Accelerator by : Jingyang Zhu

Download or read book Energy-efficient Deep Learning Accelerator written by Jingyang Zhu and published by . This book was released on 2018 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Hardware Accelerator Systems for Artificial Intelligence and Machine Learning

Author :
Publisher : Academic Press
ISBN 13 : 0128231246
Total Pages : 416 pages
Book Rating : 4.1/5 (282 download)

DOWNLOAD NOW!

Book Synopsis Hardware Accelerator Systems for Artificial Intelligence and Machine Learning by :

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