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 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 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 Applied Reconfigurable Computing Architectures Tools and Applications written by Fernando Rincón and published by Springer Nature. This book was released on 2020-03-25 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 16th International Symposium on Applied Reconfigurable Computing, ARC 2020, held in Toledo, Spain, in April 2020. The 18 full papers and 11 poster presentations presented in this volume were carefully reviewed and selected from 40 submissions. The papers are organized in the following topical sections: design methods & tools; design space exploration & estimation techniques; high-level synthesis; architectures; applications.

Download or read book Hardware Accelerator Systems for Artificial Intelligence and Machine Learning written by Shiho Kim and published by Elsevier. This book was released on 2021-04-07 with total page 414 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 Hardware for Artificial Intelligence written by Alexantrou Serb and published by Frontiers Media SA. This book was released on 2022-09-26 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Hardware Accelerators in Data Centers written by Christoforos Kachris and published by Springer. This book was released on 2018-08-21 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides readers with an overview of the architectures, programming frameworks, and hardware accelerators for typical cloud computing applications in data centers. The authors present the most recent and promising solutions, using hardware accelerators to provide high throughput, reduced latency and higher energy efficiency compared to current servers based on commodity processors. Readers will benefit from state-of-the-art information regarding application requirements in contemporary data centers, computational complexity of typical tasks in cloud computing, and a programming framework for the efficient utilization of the hardware accelerators.

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 Embedded Deep Learning written by Bert Moons and published by Springer. This book was released on 2018-10-23 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers algorithmic and hardware implementation techniques to enable embedded deep learning. The authors describe synergetic design approaches on the application-, algorithmic-, computer architecture-, and circuit-level that will help in achieving the goal of reducing the computational cost of deep learning algorithms. The impact of these techniques is displayed in four silicon prototypes for embedded deep learning. Gives a wide overview of a series of effective solutions for energy-efficient neural networks on battery constrained wearable devices; Discusses the optimization of neural networks for embedded deployment on all levels of the design hierarchy – applications, algorithms, hardware architectures, and circuits – supported by real silicon prototypes; Elaborates on how to design efficient Convolutional Neural Network processors, exploiting parallelism and data-reuse, sparse operations, and low-precision computations; Supports the introduced theory and design concepts by four real silicon prototypes. The physical realization’s implementation and achieved performances are discussed elaborately to illustrated and highlight the introduced cross-layer design concepts.

Download or read book Under Two Dictators written by Margarete Buber-Neumann and published by . This book was released on 1950 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Design and Generation of Efficient Hardware Accelerators for Sparse and Dense Tensor Computations written by Nitish Kumar Srivastava and published by . This book was released on 2020 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tensor algebra lives at the heart of big data applications. Where classical machine learning techniques such as embedding generation in recommender systems, dimensionality reduction and latent Dirichlet allocation make use of multi-dimensional tensor factorizations, deep learning techniques such as convolutional neural networks, recurrent neural networks and graph learning use tensor computations primarily in the form of matrix-matrix and matrix-vector multiplications. The tensor computations often used in many of these fields operate on sparse data where most of the elements are zeros. Traditionally, tensor computations have been performed on CPUs and GPUs, both of which have low energy-efficiency as they allocate excessive hardware resources to flexibly support various workloads. However, with the end of Moore's law and Dennard scaling, one can no longer expect more and faster transistors for the same dollar and power budget. This has led to an ever-growing need for energy-efficient and high-performance hardware that has resulted in a recent surge of interest in application-specific, domain-specific and behavior-specific accelerators, which sacrifice generality for higher performance and energy efficiency. In this dissertation, I explore hardware specialization for tensor computations by building programmable accelerators. A central theme in my dissertation is determining common spatial optimizations, computation and memory access patterns, and building efficient storage formats and hardware for tensor computations. First, I present T2S-Tensor, a language and compilation framework for productively generating high-performance systolic arrays for dense tensor computations. Then I present a versatile accelerator, Tensaurus, that can accelerate both dense and mixed sparse-dense tensor computations. Here, I also introduce a new sparse storage format that allows accessing sparse data in a vectorized and streaming fashion and thus achieves high memory bandwidth utilization for sparse tensor kernels. Finally, I present a novel sparse-sparse matrix multiplication accelerator, MatRaptor, designed using a row-wise product approach. I also show how these different hardware specialization techniques outperform CPUs, GPUs and state-of-the-art accelerators in both energy efficiency and performance.

Download or read book Embedded Devices and Internet of Things written by Adesh Kumar and published by CRC Press. This book was released on 2024-09-11 with total page 361 pages. Available in PDF, EPUB and Kindle. Book excerpt: The text comprehensively discusses machine-to-machine communication in real-time, low-power system design and estimation using field programmable gate arrays, PID, hardware, accelerators, and software integration for service applications. It further covers the recent advances in embedded computing and IoT for healthcare systems. The text explains the use of low-power devices such as microcontrollers in executing deep neural networks, and other machine learning techniques. This book: Discusses the embedded system software and hardware methodologies for system-on-chip and FPGA Illustrates low-power embedded applications, AI-based system design, PID control design, and CNN hardware design Highlights the integration of advanced 5G communication technologies with embedded systems Explains weather prediction modeling, embedded machine learning, and RTOS Highlights the significance of machine-learning techniques on the Internet of Things (IoT), real-time embedded system design, communication, and healthcare applications, and provides insights on IoT applications in education, fault attacks, security concerns, AI integration, banking, blockchain, intelligent tutoring systems, and smart technologies It is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, and computer engineering.

Download or read book Exploiting Data Characteristics in The Design of Accelerators for Deep Learning written by Patrick H. Judd and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The recent "Cambrian explosion" of Deep Learning (DL) algorithms in concert with the end of Moore's Law and Dennard Scaling has spurred interest in the design of custom hardware accelerators for DL algorithms. While DL has progressed quickly thanks in part to the abundance of efficient parallel computation provided by General Purpose Graphics Processing Units, newer DL algorithms demand even higher levels of compute density and efficiency. Furthermore, applications of DL in the mobile and embedded domains demand the energy efficiency of special purpose hardware. DL algorithms are dominated by large matrix-vector product computations, making them ideal targets for wide Single Instruction Multiple Data architectures. For the most part, efficiently mapping the structure of these computations to hardware is straightforward. Building on such designs, this thesis examines the data characteristics of these computations and proposes hardware modifications to exploit them for performance and energy efficiency. Specifically, this thesis examines the sparsity and precision requirements of Deep Convolutional Neural Networks, which comprise multiple layers of matrix-vector product computations. We propose a profiling method to find per layer reduced precision configurations while maintaining high classification accuracy. Following this, we propose three accelerator designs that build on top of the state-of-the-art DaDianNao accelerator. 1) Proteus exploits the reduced precision profiles by adding a light weight memory compression layer, saving energy in memory access and communication, and enabling larger networks in a fixed memory budget. 2) Cnvlutin exploits the presence of zero, and near zero, values in the inter-layer data by applying sparse compression to the data stream while maintain efficient utilization of the wide memory and compute structure of the SIMD accelerator. 3) Stripes exploits the reduced precision profiles for performance by processing data bit-serially, compensating for serial latency by exploiting the abundant parallelism in the convolution operation. All three designs exploit approximation, in terms of reduced precision and computation skipping to improve energy efficiency and/or performance while maintaining high classification accuracy. By approximating more aggressively, these designs can also dynamically trade-off accuracy for further improvements in performance and energy.

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.

Download or read book Hardware Accelerators for Machine Learning From 3D Manycore to Processing in Memory Architectures written by Aqeeb Iqbal Arka and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big data applications such as - deep learning and graph analytics require hardware platforms that are energy-efficient yet computationally powerful. 3D manycore architectures are the key to efficiently executing such compute- and data-intensive applications. Through silicon via (TSV)-based 3D manycore system is a promising solution in this direction as it enables integration of disparate heterogeneous computing cores on a single system. Recent industry trends show the viability of 3D integration in real products (e.g., Intel Lakefield SoC Architecture, the AMD Radeon R9 Fury X graphics card, and Xilinx Virtex-7 2000T/H580T, etc.). However, the achievable performance of conventional through-silicon-via (TSV)-based 3D systems is ultimately bottlenecked by the horizontal wires (wires in each planar die). Moreover, current TSV 3D architectures suffer from thermal limitations. Hence, TSV-based architectures do not realize the full potential of 3D integration. Monolithic 3D (M3D) integration, a breakthrough technology to achieve "More Moore and More Than Moore," and opens up the possibility of designing cores and associated network routers using multiple layers by utilizing monolithic inter-tier vias (MIVs) and hence, reducing the effective wire length. Compared to TSV-based 3D ICs, M3D offers the "true" benefits of vertical dimension for system integration: the size of a MIV used in M3D is over 100x smaller than a TSV. However, designing these new architectures often involves optimizingmultiple conflicting objectives (e.g., performance, thermal, etc.) due to thepresence of a mix of computing elements and communication methodologies; each with a different requirement for high performance. To overcome the difficult optimization challenges due to the large design space and complex interactions among the heterogeneous components (CPU, GPU, Last Level Cache, etc.) in an M3D-based manycore chip, Machine Learning algorithms can be explored as a promising solution to this problem and. The first part of this dissertation focuses on the design of high-performance and energy-efficient architectures for big-data applications, enabled by M3D vertical integration and data-driven machine learning algorithms. As an example, we consider heterogeneous manycore architectures with CPUs, GPUs, and Cache as the choice of hardware platform in this part of the work. The disparate nature of these processing elements introduces conflicting design requirements that need to be satisfied simultaneously. Moreover, the on-chip traffic pattern exhibited by different big-data applications (like many-to-few-to-many in CPU/GPU-based manycore architectures) need to be incorporated in the design process for optimal power-performance trade-off. In this dissertation, we first design a M3D-enabled heterogeneous manycore architecture and we demonstrate the efficacy of machine learning algorithms for efficiently exploring a large design space. For large design space exploration problems, the proposed machine learning algorithm can find good solutions in significantly less amount of time than exiting state-of-the-art counterparts. However, the M3D-enabled heterogeneous manycore architecture is still limited by the inherent memory bandwidth bottlenecks of traditional von-Neumann architectures. As a result, later in this dissertation, we focus on Processing-in-Memory (PIM) architectures tailor-made to accelerate deep learning applications such as Graph Neural Networks (GNNs) as such architectures can achieve massive data parallelism and do not suffer from memory bandwidth-related issues. We choose GNNs as an example workload as GNNs are more complex compared to traditional deep learning applications as they simultaneously exhibit attributes of both deep learning and graph computations. Hence, it is both compute- and data-intensive in nature. The high amount of data movement required by GNN computation poses a challenge to conventional von-Neuman architectures (such as CPUs, GPUs, and heterogeneous system-on-chips (SoCs)) as they have limited memory bandwidth. Hence, we propose the use of PIM-based non-volatile memory such as Resistive Random Access Memory (ReRAM). We leverage the efficient matrix operations enabled by ReRAMs and design manycore architectures that can facilitate the unique computation and communication needs of large-scale GNN training. We then exploit various techniques such as regularization methods to further accelerate GNN training ReRAM-based manycore systems. Finally, we streamline the GNN training process by reducing the amount of redundant information in both the GNN model and the input graph.Overall, this work focuses on the design challenges of high-performance and energy-efficient manycore architectures for machine learning applications. We propose novel architectures that use M3D or ReRAM-based PIM architectures to accelerate such applications. Moreover, we focus on hardware/software co-design to ensure the best possible performance.

Download or read book Embedded Systems and Artificial Intelligence written by Vikrant Bhateja and published by Springer Nature. This book was released on 2020-04-07 with total page 880 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers selected research papers presented at the First International Conference on Embedded Systems and Artificial Intelligence (ESAI 2019), held at Sidi Mohamed Ben Abdellah University, Fez, Morocco, on 2–3 May 2019. Highlighting the latest innovations in Computer Science, Artificial Intelligence, Information Technologies, and Embedded Systems, the respective papers will encourage and inspire researchers, industry professionals, and policymakers to put these methods into practice.