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Features the latest advances in silicon photonics for high
performance computing systems and data centers Discusses the
industries latest technologies and advances to enable silicon
photonics integration into HPC systems and data centers Describes
the latest advances in electronic-photonic cointegration and
challenges Written by internationally recognized contributors
Delves into silicon photonics design automation, challenges, and
solutions
This book presents recent advances towards the goal
of enabling efficient implementation of machine
learning models on resource-constrained systems, covering
different application domains. The focus is
on presenting interesting and new use cases of applying
machine learning to innovative application domains, exploring
the efficient hardware design of efficient machine
learning accelerators, memory optimization techniques, illustrating
model compression and neural architecture search techniques
for energy-efficient and fast execution on
resource-constrained hardware platforms, and understanding
hardware-software codesign techniques for achieving even
greater energy, reliability, and performance benefits. Discusses
efficient implementation of machine learning in embedded,
CPS, IoT, and edge computing;Â Offers comprehensive coverage
of hardware design, software design, and hardware/software
co-design and co-optimization;Â Describes real applications
to demonstrate how embedded, CPS, IoT, and edge applications
benefit from machine learning.
This book presents recent advances towards the goal of enabling
efficient implementation of machine learning models on
resource-constrained systems, covering different application
domains. The focus is on presenting interesting and new use cases
of applying machine learning to innovative application domains,
exploring the efficient hardware design of efficient machine
learning accelerators, memory optimization techniques, illustrating
model compression and neural architecture search techniques for
energy-efficient and fast execution on resource-constrained
hardware platforms, and understanding hardware-software codesign
techniques for achieving even greater energy, reliability, and
performance benefits.
This book provides comprehensive coverage of various solutions that
address issues related to real-time performance, security, and
robustness in emerging automotive platforms. The authors discuss
recent advances towards the goal of enabling reliable, secure, and
robust, time-critical automotive cyber-physical systems, using
advanced optimization and machine learning techniques. The focus is
on presenting state-of-the-art solutions to various challenges
including real-time data scheduling, secure communication within
and outside the vehicle, tolerance to faults, optimizing the use of
resource-constrained automotive ECUs, intrusion detection, and
developing robust perception and control techniques for
increasingly autonomous vehicles.
This book presents recent advances towards the goal of enabling
efficient implementation of machine learning models on
resource-constrained systems, covering different application
domains. The focus is on presenting interesting and new use cases
of applying machine learning to innovative application domains,
exploring the efficient hardware design of efficient machine
learning accelerators, memory optimization techniques, illustrating
model compression and neural architecture search techniques for
energy-efficient and fast execution on resource-constrained
hardware platforms, and understanding hardware-software codesign
techniques for achieving even greater energy, reliability, and
performance benefits. Discusses efficient implementation of machine
learning in embedded, CPS, IoT, and edge computing; Offers
comprehensive coverage of hardware design, software design, and
hardware/software co-design and co-optimization; Describes real
applications to demonstrate how embedded, CPS, IoT, and edge
applications benefit from machine learning.
While GPS is the de-facto solution for outdoor positioning with a
clear sky view, there is no prevailing technology for GPS-deprived
areas, including dense city centers, urban canyons, buildings and
other covered structures, and subterranean facilities such as
underground mines, where GPS signals are severely attenuated or
totally blocked. As an alternative to GPS for the outdoors, indoor
localization using machine learning is an emerging embedded and
Internet of Things (IoT) application domain that is poised to
reinvent the way we navigate in various indoor environments. This
book discusses advances in the applications of machine learning
that enable the localization and navigation of humans, robots, and
vehicles in GPS-deficient environments. The book explores key
challenges in the domain, such as mobile device resource
limitations, device heterogeneity, environmental uncertainties,
wireless signal variations, and security vulnerabilities.
Countering these challenges can improve the accuracy, reliability,
predictability, and energy-efficiency of indoor localization and
navigation. The book identifies severalnovel energy-efficient,
real-time, and robust indoor localization techniques that utilize
emerging deep machine learning and statistical techniques to
address the challenges for indoor localization and
navigation. In particular, the book: Provides comprehensive
coverage of the application of machine learning to the domain of
indoor localization; Presents techniques to adapt and optimize
machine learning models for fast, energy-efficient indoor
localization; Covers design and deployment of indoor localization
frameworks on mobile, IoT, and embedded devices in real conditions.
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.
This book 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.
KEY FEATURES
* 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
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