Название: Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design
Автор: Nan Zheng, Pinaki Mazumder
Издательство: Wiley-IEEE Press
Год: 2020
Страниц: 289
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB

Explains current co-design and co-optimization methodologies for building hardware neural networks and algorithms for machine learning applications.

Machine learning, especially deep learning, has emerged as an important discipline through which many conventionally difficult problems, such as pattern recognition, decision making, and natural language processing, can be addressed. Nowadays, millions and even billions of neural networks are running in data centers, personal computers and portable devices to perform various tasks. In the future, it is expected that more complex neural networks with larger sizes will be needed. Such a trend demands specialized hardware to accommodate the ever-increasing requirements on power consumption and response time.

In this book, we focus on the topic of how to build energy-efficient hardware for neural networks with a learning capability. This book strives to provide co-design and co-optimization methodologies for building hardware neural networks that can learn to perform various tasks. The book provides a complete picture from high-level algorithms to low-level implementation details. Hardware-friendly algorithms are developed with the objective to ease implementation in hardware, whereas special hardware architectures are proposed to exploit the unique features of the algorithms.

- 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.

Contents: