DeepBrain Chain ($DBC), also known as DeepBrain Chain, is a decentralized high-performance GPU computing network that can be infinitely expanded. It integrates and provides idle computing power and data resources from around the world, offering a secure and economical guarantee for the development of AI applications. Its goal is to become the most widely used GPU computing infrastructure in the global AI and metaverse era.

Project Background

Project Background:
DeepBrain Chain officially launched in 2017, completed its fundraising and was listed on Huobi Exchange in 2018. By 2020, it had provided services to over 500 AI-related universities and laboratories worldwide. In 2021, the mainnet for nodes was officially launched. This year, DeepBrain Chain plans to develop a short-term GPU rental model and achieve token interactions within the network.

Core Team Members

He Yong: Chairman and CEO of DeepBrain Chain Foundation, AI expert, and an innovative figure in Shanghai’s computer industry. He began researching Bitcoin and blockchain technology in 2014. He is proficient in product design and machine learning algorithms and is the inventor of an intelligent semantic error correction engine commonly used in game translation. He Yong is also one of the first entrepreneurs in China’s AI field and led the development of the world’s first AI speaker.

Wang Dongyan: Chief AI Officer of DeepBrain Chain. He is an expert with nearly 20 years of experience in artificial intelligence, business intelligence, and data science in Silicon Valley. Dr. Wang Dongyan has led top technical teams for Fortune 500 companies (Cisco, NetApp, Midea Group, Samsung) and has won numerous awards.

Brain Xu: Chief Data Scientist of DeepBrain Chain. Since 1998, he has had extensive experience with over 48 products (AI, ML, and data analysis, etc.) in the software field. He has developed 20 projects for major clients (Boeing, DARPA, etc.), authored 38 technical papers and US patents, and produced 76 technical reports.

Jason Pai: Senior Product Expert and Director of AI Mining Machines at Silicon Valley Labs. He holds a Master’s in Business Analytics from New York University’s Stern School of Business and a Master’s in Industrial Engineering from Arizona State University’s Fulton School of Engineering, focusing on technology management and operations research. Before joining DeepBrain Chain, Jason had 15 years of experience in hardware R&D and product management, having worked at Supermicro, IBM, and Ford Motor Company.

DeepBrain Chain Data

DeepBrain Chain Data (Source: DeepBrain Chain)

As of July 30, 2024, there are 810 GPUs in the computing power mainnet, with 11 computing pools participating. The total computing power value has reached 272,731.31. These GPUs have collectively staked 80,544,779 $DBC, with a GPU rental rate of 77.41%. Over 200,000 addresses hold $DBC, with nearly 1.3 billion tokens staked.

DeepBrain Chain Structure and Optimization

DeepBrain Chain Network Structure:

1. The entire network structure is divided into the main chain (DeepBrain Chain) + relay nodes + work chains/side chains.
2. The DeepBrain Chain, as the main chain, will be responsible for all transactions (cross-chain) of the work chains/side chains.
3. Relay nodes are responsible for connecting the main chain and the work chains/side chains.
4. Work chains/side chains will independently handle their respective business applications and commercial needs. Through relay nodes, the main chain and the work chains/side chains can achieve bi-directional anchoring and conversion.

DeepBrain Chain Structure Diagram (Source: DeepBrain Chain)

As a distributed high-performance computing network, DeepBrain Chain essentially builds the infrastructure for the 5G+AI era. Current blockchains face issues such as insufficient performance, scalability, upgrade difficulties, and lack of infrastructure. DeepBrain Chain has made numerous technical optimizations to address these existing blockchain issues:

1. Matrix Platform and Topic-specific Software Architecture: DeepBrain Chain adopts the Matrix platform and unique software architecture of Topic.
2. Layered Blockchain Architecture: The architecture is divided into storage, network, and computing layers, each processed in a layered, pipelined manner. Each layer uses a highly scalable architecture supporting elastic scaling.
3. Multi-chain Framework: Consists of one main chain and multiple work chains. The main chain includes the scheme definitions for all work chains, with no limit to the number of work chains. Work chains are composed of sharded blockchains, supporting unlimited sharding.
4. Transaction Compression: Supports compressed storage and transmission of transaction data, improving transmission efficiency by 40%.
5. Dual-layer Transmission Protocol: Uses a self-encoding dual-layer transmission protocol or dual-layer encryption transmission protocol, reducing network bandwidth. The encoding protocol of different message packets on the same link can be freely changed, making transmission more secure.
6. Multi-layer Network: Built on the P2P network, introduces relay nodes and adopts a multi-layer network message routing mechanism, improving the overall network transmission efficiency and connectivity.
7. AI-POC (Proof of Contribution) Consensus Mechanism: Based on AI algorithms, this mechanism uses an algorithm that proves contributions based on user assets and participation level.

DeepBrain Chain Application Ecosystem

Anyone can build their own GPU cloud service platform based on the DeepBrain network. DeepBrain Chain aims to establish a comprehensive ecosystem, generating AI data trading platforms, AI algorithm trading platforms, AI model trading platforms, AI container trading platforms, and AI application trading platforms.

AI Training:
AI training involves using large amounts of data and algorithms to train networks. The goal is to obtain a model capable of making predictions. The market size for GPU servers used for AI training is expected to reach $12 billion by 2024 and continue to grow.

AI Inference:
AI inference allows trained AI models to make predictions based on new data. The market is expected to reach $8 billion this year and will continue to grow over time.

Cloud Gaming:
Cloud gaming services allow games to be rendered and processed through cloud-based GPU servers, with the game images streamed to players’ devices. Cloud gaming enables any AAA game to run on any device.

Visual Rendering:
Visual rendering solutions are primarily used in the film and 3D animation industries. The global market size reached $723.7 million in 2023 and is expected to grow rapidly this year.

Cloud Cafes:
Cloud cafes are a new type of internet café service based on cloud computing technology. In cloud cafes, games and applications run on remote GPU servers and are streamed in real-time to the café’s computers. Internet café operators do not need to invest in high-performance GPU hardware, significantly reducing hardware investment costs. As of 2023, there were over 200,000 internet cafés worldwide with a total of 10 million computers.

ZK Mining:
ZK Mining refers to projects such as Filecoin, Aleo, and Ethereum Layer 2 networks that require GPU servers for zero-knowledge proof calculations.

$DBC Token Economic Model

The native token of DeepBrain Chain, $DBC, has a total issuance of 10 billion tokens. 40% of the total supply is generated through mining, with the entire supply expected to be fully issued within 100 years. $DBC follows a deflationary model: when the total number of GPUs in the DeepBrain Chain network is below 5,000, 30% of the user rental fees are burned. When the number exceeds 5,000, the burn rate increases to 70%, and when it exceeds 10,000, the burn rate reaches 100%.

$DBC Allocation Breakdown：

$DBC Allocation (Source: DeepBrain Chain):

15% for early sales

17.35% for the DBC Foundation

10% for the team

10% for computing power incentives before mainnet launch

7.65% for the DBC Council

8% for supernodes

32% for node rewards

$DBC Token Use Cases

• GPU Rentals: Whenever users rent GPUs, they need to purchase $DBC from exchanges and then pay a certain amount of $DBC to DeepBrain Chain to gain GPU usage rights. The token essentially includes the rental fees.
• Voting Rights: Each $DBC token grants one vote, allowing holders to participate in governance decisions.
• Data Transactions: $DBC can be used to buy and sell data, which may cover areas such as AI training and market analysis.
• Ecosystem Rewards: $DBC can be used to reward ecosystem participants, such as developers, miners, and node operators, to promote the development and operation of the platform.

How to Obtain $DBC Tokens

To obtain $DBC tokens, you can purchase them through cryptocurrency exchanges. For instance, the reputable Gate.io exchange supports $DBC purchases. You simply need to create a Gate.io account, complete the KYC process, and then deposit funds into your account to directly purchase $DBC tokens.

Future Development

DeepBrain Chain is actively expanding into overseas markets. A recent meetup in Seoul attracted numerous investors, media developers, and industry professionals, resulting in a successful event. The company is also advancing into overseas application markets such as Singapore and Vietnam. Following the establishment of DBC-IDC in Korea, the rollout of cloud cafes is also underway.

Additionally, DeepBrain Chain aims to promote the global adoption of decentralized AI and GPU capabilities. It seeks to foster collaboration among AI developers, GPU providers, and investors, creating and maintaining a vibrant and innovative ecosystem. This approach not only adapts to the future of AI but also actively shapes it, driving global innovation and efficiency.

Conclusion

DeepBrain Chain assists AI practitioners, enterprises, universities, research institutions, cloud gaming, rendering, and blockchain users in reducing computing costs, improving computational efficiency, and enhancing product experiences. Significant progress has been made in the fields of GPU cloud platforms, distributed computing networks, and the mainnet.

DeepBrain Chain provides fast, economical, and secure services to the global AI community. By integrating Web3 technology, it effectively addresses several challenges in the AI application domain, such as privacy and high costs, and continues to expand its application scope. The DeepBrain Chain team, with its high reputation and extensive experience in the AI industry, is now actively expanding into overseas markets and developing the network ecosystem. If you are optimistic about the future of the AI industry, it might be worth considering an early investment in DeepBrain Chain.