类脑超大规模深度神经网络系统

Large-scale deep neural networks (DNNs) exhibit powerful end-to-end representation and infinite approximation of nonlinear functions, showing excellent performance in several fields and becoming an important development direction. For example, the natural language processing model GPT, after years of development, now has 175 billion network parameters and achieves state-of-the-art performance on several NLP benchmarks. However, according to the existing deep neural network organization, the current large-scale network is difficult to reach the scale of human brain biological neural network connection. At the same time, the existing large-scale DNNs do not perform well in multi-channel collaborative processing, knowledge storage, and reasoning. This study proposes a brain-inspired large-scale DNN model, which is inspired by the division and the functional mechanism of brain regions and built modularly by the functional of the brain, integrates a large amount of existing data and pre-trained models, and proposes the corresponding learning algorithm by the functional mechanism of the brain. The DNN model implements a pathway to automatically build a DNN as an output using the scene as an input. Simultaneously, it should not only learn the correlation between input and output but also needs to have the multi-channel collaborative processing capability to improve the correlation quality, thereby realizing knowledge storage and reasoning ability, which could be treated as a way toward general artificial intelligence. The whole model and all data sets and brain-inspired functional areas are managed by a database system which is equipped with the distributed training algorithms to support the efficient training of the large-scale DNN on computing clusters. This study also proposes a novel idea to implement general artificial intelligence, and the large-scale model is validated on several different modal tasks.