About DeepBrain Chain (DBC)
DeepBrain Chain (DBC) AI training and testing network released, introducing key companies into closed testing; Start network beta testing, public API for third party users mainnet was officially launched, and online reward smart contract was officially launched
DeepBrain Chain (DBC) 2017 Q3DeepBrain chain Token Sale & Whitepaper 1.0 release in August, 2017Listing of DBC on exchanges in December, 20172017 Q42018 Q1 Establishment of a research laboratory in the Silicon Valley in February, 2018. To achieve AI training in P2P network The mainnet token swap of the exchange is completed, and the Galaxy Race is launched and the high-performance computing power network of the DeepBrain Chain realized complete decentralized supply and renting
DeepBrain Chain (DBC) Storage Key Points
|Coin Name||DeepBrain Chain|
|Circulating Supply||3.20B DBC|
|Source Code||Click Here To View Source Code|
|Explorers||Click Here To View Explorers|
|Chat||Click Here To Visit|
|Whitepaper||Click Here To View|
|Official Project Website||Click Here To Visit Project Website|
DeepBrainChain was established in November 2017. The vision is to build the world’s largest distributed high-performance computing network based on blockchain technology and become the most important infrastructure in the 5G+AI era. DeepBrainChain consists of two important parts: the high-performance computing power network and the blockchain main network. The high-performance computing power network was officially launched in August 2018, and the blockchain main network was officially launched on May 20, 2021, Beijing time.
The main network of DeepBrainChain is developed based on Polkadot Substrate. DeepBrainChain is also one of the few high-performance computing projects that have achieved large-scale implementation in the blockchain industry. Since its establishment, DeepBrain Chain has been committed to improving the ease of
Chain’s computing power network. Usability and promotion of commercialization, and made great progress. It has been widely used in various scenarios such as blockchain, artificial intelligence, cloud games, visual rendering, biopharmaceuticals, semiconductor simulation and simulation, and has provided many companies with high Cost-effective GPU computing power. Accumulatively, more than 50 manufacturers around the world have deployed hig performance GPU cloud platforms based on the Chain network, serving hundreds of companies and tens of thousands of AI developer groups.
DeepBrain Chain’s vision is to build the infrastructure for the 5G + AIoT era which is aimed at providing all industries with low-cost, private and secure high-performance computing power. DeepBrain Chain’s mission is to accelerate the advancement of artificial intelligence in an era that is undergoing an explosion of smart devices, the data they acquire and their computational needs.
1. DeepBrain Chain allows the artificial intelligence neural network operation to be decentralized and distributed over the mass nodes of the whole world through blockchain technology. Thus, the cost can be just 30% of the user’s self-built neural network server; and less than 50% of the traditional artificial intelligence centralization cloud computing platform.
2. Through smart contracts, data providers and data training parties will be physically separated, protecting data privacy. This successfully resolves the trust issue that often prevents data providers from willing to share the proprietary data for training, which is essential for developing AI products.
3. Using DBC token as the universal currency of the platform, DeepBrain Chain maximizes participation from various AI players in the ecosystem all around the world, including developers, universities, computing power providers, SMEs, financial institutes, Data providers etc
Their system saves enterprises up to 70% computing power costs.
Insulated data transaction environment secured by encryption algorithms and smart contracts.
Separating data ownership from data usage right using smart contracts.
Artificial intelligence products need to train models by neural network calculation, and the data model training process needs to consume a large amount of computing resources. Also, Artificial intelligence products want to achieve better product index, in addition to the algorithm. That is, there is a need for massive data to train, but more data, in the case of equal computing resources, means longer training, say over a week or even a month to several months. If there are incorrect parameters in the training process, repeated training is needed. Long training time is extremely disadvantageous to the enterprise product’s iterative updating, increasing the product’s likelihood to fail in the industry’s competition. This leads to the fact that many manufacturers have to invest a lot of money to purchase GPU, FPGA, and other hardware resources, directly causing the artificial intelligence chip provider’s, e.g. NVIDIA’s, share price to rise rapidly. For most small and medium enterprises, more than one million of capital investment is a huge