Marian Verhelst – författare
1 091 kr
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1 408 kr
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Embedded Deep Learning
Algorithms, Architectures and Circuits for Always-on Neural Network Processing
982 kr
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874 kr
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847 kr
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This book proposes probabilistic machine learning models that represent the hardware properties of the device hosting them. These models can be used to evaluate the impact that a specific device configuration may have on resource consumption and performance of the machine learning task, with the overarching goal of balancing the two optimally.
The book first motivates extreme-edge computing in the context of the Internet of Things (IoT) paradigm. Then, it briefly reviews the steps involved in the execution of a machine learning task and identifies the implications associated with implementing this type of workload in resource-constrained devices. The core of this book focuses on augmenting and exploiting the properties of Bayesian Networks and Probabilistic Circuits in order to endow them with hardware-awareness. The proposed models can encode the properties of various device sub-systems that are typically not considered by other resource-aware strategies, bringing about resource-saving opportunities that traditional approaches fail to uncover.
The performance of the proposed models and strategies is empirically evaluated for several use cases. All of the considered examples show the potential of attaining significant resource-saving opportunities with minimal accuracy losses at application time. Overall, this book constitutes a novel approach to hardware-algorithm co-optimization that further bridges the fields of Machine Learning and Electrical Engineering.
657 kr
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874 kr
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840 kr
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This book focuses on the acceleration of emerging irregular sparse workloads, posed by novel artificial intelligent (AI) models and sparse linear algebra. Specifically, the book outlines several co-optimized hardware-software solutions for a highly promising class of emerging sparse AI models called Probabilistic Circuit (PC) and a similar sparse matrix workload for triangular linear systems (SpTRSV). The authors describe optimizations for the entire stack, targeting applications, compilation, hardware architecture and silicon implementation, resulting in orders of magnitude higher performance and energy-efficiency compared to the existing state-of-the-art solutions. Thus, this book provides important building blocks for the upcoming generation of edge AI platforms.
657 kr
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1 416 kr
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1 672 kr
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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.
1 091 kr
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Embedded Deep Learning
Algorithms, Architectures and Circuits for Always-on Neural Network Processing
1 416 kr
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Embedded Deep Learning
Algorithms, Architectures and Circuits for Always-on Neural Network Processing
1 214 kr
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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.1 633 kr
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1 985 kr
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Smart energy management, both at design time and at run time, is indispensable in modern radios. It requires a careful trade-off between the system’s performance, and its power consumption. Moreover, the design has to be dynamically reconfigurable to optimally balance these parameters at run time, depending on the current operating conditions.
Energy Scalable Radio Design describes and applies an energy-driven design strategy to the design of an energy-efficient, highly scalable, pulsed UWB receiver, suitable for low data rate communication and sub-cm ranging. This book meticulously covers the different design steps and the adopted optimizations: System level air interface selection, architectural/algorithmic design space exploration, algorithmic refinement (acquisition, synchronization and ranging algorithms) and circuit level (RTL) implementation based on the FLEXmodule-concept. Measurement results demonstrate the effectiveness and necessity of the energy-driven design strategy.
1 633 kr
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