Invention Grant
US09189729B2 Scalable neural hardware for the noisy-OR model of Bayesian networks
有权
贝叶斯网络噪声或模型的可扩展神经硬件
- Patent Title: Scalable neural hardware for the noisy-OR model of Bayesian networks
- Patent Title (中): 贝叶斯网络噪声或模型的可扩展神经硬件
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Application No.: US13562187Application Date: 2012-07-30
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Publication No.: US09189729B2Publication Date: 2015-11-17
- Inventor: John V. Arthur , Steven K. Esser , Paul A. Merolla , Dharmendra S. Modha
- Applicant: John V. Arthur , Steven K. Esser , Paul A. Merolla , Dharmendra S. Modha
- Applicant Address: US NY Armonk
- Assignee: International Business Machines Corporation
- Current Assignee: International Business Machines Corporation
- Current Assignee Address: US NY Armonk
- Agency: Sherman IP LLP
- Agent Kenneth L. Sherman; Hermavathy Perumal
- Main IPC: G06N3/04
- IPC: G06N3/04 ; G06N3/08 ; G06F7/58 ; G06N7/00

Abstract:
Embodiments of the invention relate to a scalable neural hardware for the noisy-OR model of Bayesian networks. One embodiment comprises a neural core circuit including a pseudo-random number generator for generating random numbers. The neural core circuit further comprises a plurality of incoming electronic axons, a plurality of neural modules, and a plurality of electronic synapses interconnecting the axons to the neural modules. Each synapse interconnects an axon with a neural module. Each neural module receives incoming spikes from interconnected axons. Each neural module represents a noisy-OR gate. Each neural module spikes probabilistically based on at least one random number generated by the pseudo-random number generator unit.
Public/Granted literature
- US20150286924A1 SCALABLE NEURAL HARDWARE FOR THE NOISY-OR MODEL OF BAYESIAN NETWORKS Public/Granted day:2015-10-08
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