- 专利标题: SYMBOLIC VALIDATION OF NEUROMORPHIC HARDWARE
-
申请号: US17077759申请日: 2020-10-22
-
公开(公告)号: US20220129436A1公开(公告)日: 2022-04-28
- 发明人: Alexander Andreopoulos , Dharmendra S. Modha , Andrew Stephen Cassidy , Brian Seisho Taba , Carmelo Di Nolfo , Hartmut Penner , John Vernon Arthur , Jun Sawada , Myron D. Flickner , Pallab Datta , Rathinakumar Appuswamy
- 申请人: INTERNATIONAL BUSINESS MACHINES CORPORATION
- 申请人地址: US NY Armonk
- 专利权人: INTERNATIONAL BUSINESS MACHINES CORPORATION
- 当前专利权人: INTERNATIONAL BUSINESS MACHINES CORPORATION
- 当前专利权人地址: US NY Armonk
- 主分类号: G06F16/23
- IPC分类号: G06F16/23 ; G06N3/04
摘要:
Systems are provided that can produce symbolic and numeric representations of the neural network outputs, such that these outputs can be used to validate correctness of the implementation of the neural network. In various embodiments, a description of an artificial neural network containing no data-dependent branching is read. Based on the description of the artificial neural network, a symbolic representation is constructed of an output of the artificial neural network, the symbolic representation comprising at least one variable. The symbolic representation is compared to a ground truth symbolic representation, thereby validating the neural network system.
信息查询