- 专利标题: MEMORY REDUCTION FOR NEURAL NETWORKS WITH FIXED STRUCTURES
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申请号: US15943079申请日: 2018-04-02
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公开(公告)号: US20190303025A1公开(公告)日: 2019-10-03
- 发明人: Taro Sekiyama , Haruki Imai , Jun Doi , Yasushi Negishi
- 申请人: INTERNATIONAL BUSINESS MACHINES CORPORATION
- 主分类号: G06F3/06
- IPC分类号: G06F3/06 ; G06N3/08
摘要:
A method is provided for reducing consumption of a memory in a propagation process for a neural network (NN) having fixed structures for computation order and node data dependency. The memory includes memory segments for allocating to nodes. The method collects, in a NN training iteration, information for each node relating to an allocation, size, and lifetime thereof. The method chooses, responsive to the information, a first node having a maximum memory size relative to remaining nodes, and a second node non-overlapped with the first node lifetime. The method chooses another node non-overlapped with the first node lifetime, responsive to a sum of memory sizes of the second node and the other node not exceeding a first node memory size. The method reallocates a memory segment allocated to the first node to the second node and the other node to be reused by the second node and the other node.
公开/授权文献
- US10782897B2 Memory reduction for neural networks with fixed structures 公开/授权日:2020-09-22
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