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.
