1.3.2 Computing Power

 The increase in computing power is an important factor in the third artificial intelligence renaissance. In fact, the basic theory of modern deep learning was proposed in the 1980s, but the real potential of deep learning was not realized until the release of AlexNet based on training on two GTX580 GPUs in 2012. Traditional machine learning algorithms do not have stringent requirements on data volume and computing power like deep learning. Usually, serial training on CPU can get satisfactory results. But deep elarning relies heavily on parallel acceleration computing devices. Most of current neural networks use parallel acceleration chips such as NVIDIA GPU and Google TPU to train model parameters. For example, the AlphaGo Zero program needs to be trained on 64 GPUs from scratch for 40 days before surpassing all AlphaGo historical versions. The automatic network structure search algorithm used 800 GPU s to optimize a better network strtucture.

At present, the deep elarnign acceleration hardware device that ordinary consumers can sue are mainly from NVIDIA GPU from 2008 to 2017. It can be seen that the curve of x86 CPU changes relatively slowly, and the floating-point computing capacity of NVIDIA GPU grows exponentially which is mainly driven by the increasing business of game and deep learning computing.