Another small advancement in artificial intelligence _ Kirin 970: artificial intelligence chip NPU_ pro test Mate10 mobile phone Kirin 970 performance
The era of artificial intelligence is a continuation and new life of the information age. Every small progress in this field will accumulate today, until we open the door to a great era.
Nearly 100 billion brain neurons have been produced and differentiated at birth, and they are in their place of existence, waiting to establish neural networks. At this time, the neurons have not had many abrupt changes, and there are not many links between them. The number of neonatal synapses is less than one-third of that of adults. During the growth and development of infants and young children, neurons rapidly extend out of the catenoids, establishing very complex links with each other. Connected neurons can transmit electrical and chemical signals. When approaching the age of three, the number of thixes reaches its peak. The number of times is twice that of adults. This period of time is also the fastest period of human learning and cognition. Our basic survival skills, such as language, image recognition and classification, and movement, must be established at this time. In the following period, the brain constantly optimizes and trims the links of neurons to make the processing of information more efficient. In the process of establishing a neural network, the establishment of links is random, but the pruning is not random, and the brain constantly performs according to external information and feedback. The resulting neural network is complex and can process data efficiently, even if identical twins growing up in the same environment have two completely different brains.
Imitating the neural computing network established by the biological nervous system, the basic principle is to process and filter information layer by layer. The basic operations of each layer are very simple. It is nothing more than adding and multiplying to complete the matrix operation, but the various parameters in the operation. The factor is an unknown number. It can only let the neural network learn by itself through a large amount of data. This learning process is the same as the child learning to map. A large number of various forms of image data are input, and the final output is corrected. Once the output error is found. Try to go back to the new parameter settings until you find the output of the composite request. Therefore, the neural network only focuses on the problem of "solvable" and does not emphasize the "optimal solution". Each time the problem is solved and solved, the network parameters can be further optimized to adapt the network to more complex data input.
Until 2016, AlphaGo of the GoogleDeepmind team defeated the professional nine-part Li Shishi, and set off the attention of artificial neural networks around the world. This is a complete victory. AlphaGo's developers don't understand Go. Open AlphaGo program and no one can read the various parameters of Tianshu. AlphaGo completely learns Go by itself to achieve the ability to defeat human beings. The original AlphaGo uses CPU + GPU as the computing unit. With the increase of the amount of computing, the GPU is not designed for neural network computing. Google designed the computing unit TPU to improve efficiency. The TPU is tailor-made for the deep learning framework TensorFlow. The AlphaGo with TPU is upgraded faster, and only four TPUs in a single game will crush all the top players in the subsequent games.
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