With the breakthrough of algorithms, the improvement of computing power and the accumulation of data, artificial intelligence technology has ushered in a leap-forward development in recent years. Through a full range of commercialization practices, it has gradually changed the original industrial structure, triggered profound changes…
With the breakthrough of algorithms, the improvement of computing power and the accumulation of data, artificial intelligence technology has ushered in a leap-forward development in recent years. Through a full range of commercialization practices, it has gradually changed the original industrial structure, triggered profound changes within various industries, and even changed the way we interact with the outside world.
The implementation of artificial intelligence algorithms requires strong computing power support, especially the large-scale use of deep learning algorithms, which puts higher demands on computing power. Since 2012, the demand for computing power in artificial intelligence training has continued to grow, doubling every three and a half months, and has grown by more than 300,000 times.
Currently, the demand for artificial intelligence is reflected in two main directions. One is a centralized hyper-scale data computing center (cloud computing), and artificial intelligence requires a lot of computing power in the algorithm optimization phase.
The other direction is based on edge computing (fog computing) in the field of artificial intelligence applications. In the mobile era, a large amount of data in the local storage computing mode can no longer meet the needs of users. Therefore, the computing power will be tilted to the edge with the development of mobile devices and IoT smart devices, showing a trend of distributed deployment. Full coverage is formed like a mobile communication base station, and the separation between the client and the server is achieved.
Thanks to the development of technologies such as blockchain and 5G, many problems faced by fog computing itself will be solved. 5G technology solves the problem of data transmission rate. The blockchain’s encryption properties perfectly solve the personal data security problem. Its incentive mechanism can also attract many distributed computing nodes to form a fog computing network that is wide enough to extend to every corner.
With the further development of artificial intelligence research and application, and the coordinated development of the fields of Internet of Things, intelligent manufacturing, and 5G, the computing power network combining cloud computing and fog computing is bound to become the same infrastructure as communications, power, and network.
In the stage of the outbreak of artificial intelligence computing needs, TuringFog（turingfog.ai） has created a unified resource computing platform for resource-intensive and service artificial intelligence industry based on blockchain technology. Make full use of the economic drivers of blockchain and the productivity of new technologies such as 5G to coordinate the interests of all parties involved. Based on the cloud-integrated technology architecture, physical resources such as computing power and perception are shared and shared, providing more efficient and reliable foundation support for the artificial intelligence industry and the fourth industrial revolution.
In the Turing Fog network, any user can become the seller and renter of the computing power. Whether you are providing an idle home computer or a few large computing centers, you can join the Turing Fog Network.
Distributed joint machine learning developed by Turing Fog enables distributed computing to jointly perform algorithmic tasks. At present, the computing performance of each terminal GPU is improved, so that each device is a computing resource that cannot be ignored. With the support of blockchain and 5G technology, Turing Fog can closely integrate distributed computing power, large-scale data and algorithms to create a network of valuable infrastructure.
Because there is no uniform unit of measurement, the computing resources always rely on third-party media in the process of circulation or have been traded in the form of a server entity. It is the first time that Turing Fog defines an objective measurement unit for the production node: Turing Unit (TU). Simply speaking, one TU equals the 24-hour continuous computational power of the GPU GTX 1080 Ti. Thus, the computing resources can directly enter the stage of trading circulation and have a clear value evaluation system.
Turing fog performs TensorFlow’s single-precision deep learning training on different mainstream computing platforms. Each GPU trains ResNet-50, ResNet-152, Inception v3, Inception v4, VGG-16, AlexNet and SSD300 on a single GPU, and records the number of pictures per second that can be processed during the training network as a comparison value, so you can easily calculate the computing power units for each GPU.
The formula for calculating the power of the GPU：
Main GPU computing power：（Unit: TU）
|GTX 1080 Ti||Titan XP||V100||Titan V||Titan RTX||RTX 2080||RTX 2080 Ti|
Fig Turing Fog TFT Point System
The Turing Fog establishes the Turing Fog Token (TFT) and uses the TFT as the native token. The way of using computing power is that users can purchase the power with fiat currency or TFT, and all the funds received will be used to repurchase and burn TFT from the secondary market.
At the same time, 80% of the burned TFT will be released from the portion of production mining, which will be distributed to the miners and cover the platform operation cost. When production mining has been finished, the project side will repurchase TFT from the open market, and half of the TFT will be burned, and the other half will be distributed to the miners. The released or unlocked TFT can flow to the open market, which is “destroy equals to mining”. Under this mechanism, the TFT will always remain in a deflated state.
The nominal total amount of TFT is 1 billion, and the theoretical upper limit is about 650 million pieces, but the actual upper limit is far less than 650 million. After several years of “destroy equals to mining” mechanism, the ultimate limit will be reduced to 100 million. At this time, “destroy equals to mining” plan will stop, and Turing Fog will enter the stage of ecological maturity and stable development.
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Last modified: January 14, 2020 12:43 PM UTC