| 书目名称 | VLSI for Neural Networks and Artificial Intelligence |
| 编辑 | José G. Delgado-Frias,William R. Moore |
| 视频video | |
| 图书封面 |  |
| 描述 | Neural network and artificial intelligence algorithrns and computing have increased not only in complexity but also in the number of applications. This in turn has posed a tremendous need for a larger computational power that conventional scalar processors may not be able to deliver efficiently. These processors are oriented towards numeric and data manipulations. Due to the neurocomputing requirements (such as non-programming and learning) and the artificial intelligence requirements (such as symbolic manipulation and knowledge representation) a different set of constraints and demands are imposed on the computer architectures/organizations for these applications. Research and development of new computer architectures and VLSI circuits for neural networks and artificial intelligence have been increased in order to meet the new performance requirements. This book presents novel approaches and trends on VLSI implementations of machines for these applications. Papers have been drawn from a number of research communities; the subjects span analog and digital VLSI design, computer design, computer architectures, neurocomputing and artificial intelligence techniques. This book has been |
| 出版日期 | Book 1994 |
| 关键词 | CMOS; VLSI; artificial intelligence; backpropagation; knowledge; learning; neural network |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-1-4899-1331-9 |
| isbn_softcover | 978-1-4899-1333-3 |
| isbn_ebook | 978-1-4899-1331-9 |
| copyright | Springer Science+Business Media New York 1994 |
发表于 2025-3-21 17:05:13
