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.