|
Showing 1 - 14 of
14 matches in All Departments
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 focuses on two of the most relevant problems related to
power management on multicore and manycore systems. Specifically,
one part of the book focuses on maximizing/optimizing computational
performance under power or thermal constraints, while another part
focuses on minimizing energy consumption under performance (or
real-time) constraints.
This book describes novel software concepts to increase reliability
under user-defined constraints. The authors' approach bridges, for
the first time, the reliability gap between hardware and software.
Readers will learn how to achieve increased soft error resilience
on unreliable hardware, while exploiting the inherent error masking
characteristics and error (stemming from soft errors, aging, and
process variations) mitigations potential at different software
layers.
This book presents techniques for energy reduction in adaptive
embedded multimedia systems, based on dynamically reconfigurable
processors. The approach described will enable designers to meet
performance/area constraints, while minimizing video quality
degradation, under various, run-time scenarios. Emphasis is placed
on implementing power/energy reduction at various abstraction
levels. To enable this, novel techniques for adaptive energy
management at both processor architecture and application
architecture levels are presented, such that both hardware and
software adapt together, minimizing overall energy consumption
under unpredictable, design-/compile-time scenarios.
This book shows readers how to develop energy-efficient algorithms
and hardware architectures to enable high-definition 3D video
coding on resource-constrained embedded devices. Users of the
Multiview Video Coding (MVC) standard face the challenge of
exploiting its 3D video-specific coding tools for increasing
compression efficiency at the cost of increasing computational
complexity and, consequently, the energy consumption. This book
enables readers to reduce the multiview video coding energy
consumption through jointly considering the algorithmic and
architectural levels. Coverage includes an introduction to 3D
videos and an extensive discussion of the current state-of-the-art
of 3D video coding, as well as energy-efficient algorithms for 3D
video coding and energy-efficient hardware architecture for 3D
video coding.
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.
This book provides readers with a comprehensive, state-of-the-art
overview of approximate computing, enabling the design trade-off of
accuracy for achieving better power/performance efficiencies,
through the simplification of underlying computing resources. The
authors describe in detail various efforts to generate approximate
hardware systems, while still providing an overview of support
techniques at other computing layers. The book is organized by
techniques for various hardware components, from basic building
blocks to general circuits and systems.
This book focuses on two of the most relevant problems related to
power management on multicore and manycore systems. Specifically,
one part of the book focuses on maximizing/optimizing computational
performance under power or thermal constraints, while another part
focuses on minimizing energy consumption under performance (or
real-time) constraints.
This book describes novel software concepts to increase reliability
under user-defined constraints. The authors' approach bridges, for
the first time, the reliability gap between hardware and software.
Readers will learn how to achieve increased soft error resilience
on unreliable hardware, while exploiting the inherent error masking
characteristics and error (stemming from soft errors, aging, and
process variations) mitigations potential at different software
layers.
This book shows readers how to develop energy-efficient algorithms
and hardware architectures to enable high-definition 3D video
coding on resource-constrained embedded devices. Users of the
Multiview Video Coding (MVC) standard face the challenge of
exploiting its 3D video-specific coding tools for increasing
compression efficiency at the cost of increasing computational
complexity and, consequently, the energy consumption. This book
enables readers to reduce the multiview video coding energy
consumption through jointly considering the algorithmic and
architectural levels. Coverage includes an introduction to 3D
videos and an extensive discussion of the current state-of-the-art
of 3D video coding, as well as energy-efficient algorithms for 3D
video coding and energy-efficient hardware architecture for 3D
video coding.
This book presents techniques for energy reduction in adaptive
embedded multimedia systems, based on dynamically reconfigurable
processors. The approach described will enable designers to meet
performance/area constraints, while minimizing video quality
degradation, under various, run-time scenarios. Emphasis is placed
on implementing power/energy reduction at various abstraction
levels. To enable this, novel techniques for adaptive energy
management at both processor architecture and application
architecture levels are presented, such that both hardware and
software adapt together, minimizing overall energy consumption
under unpredictable, design-/compile-time scenarios.
|
You may like...
Loot
Nadine Gordimer
Paperback
(2)
R383
R318
Discovery Miles 3 180
Poor Things
Emma Stone, Mark Ruffalo, …
DVD
R343
Discovery Miles 3 430
|