EMC: A New Force Driving the Integration of AI Technology and Blockchain
Introduction
Integrating artificial intelligence and blockchain technology is becoming a new focal point in the rapidly evolving wave of technology. The narrative of building a computing power DePin based on Graphics Processing Units (GPUs) is beginning to create a new wave in the Web3 space.
In recent years, the widespread application of AI technology has led to a growing demand for computing power resources across various industries. However, the monopoly of high-performance GPUs in the market has made it difficult for many small and medium-sized enterprises to obtain the necessary computing support. Based on this demand trend, the EMC (Edge Matrix Computing) project was born, aiming to solve the problem of insufficient computing power allocation by integrating idle graphics card resources from around the world.
The EMC team has pioneered the “DeAI” concept, distinguishing it from traditional GPU cloud services. The project provides an efficient AI training model through its computing power scheduling platform, which enables developers to access computing resources at low costs. This innovation promotes the integration of AI and blockchain in resource utilization and data sharing, empowering the development of the Web3 ecosystem and creating real application value.
What is EMC?
Project Overview
EMC (Edge Matrix Computing) was established in 2022 as a high-performance decentralized AI computing application network, aiming to address the friction between the development of AI technology and GPU computing power resources. As of October 2024, it has built a computing power network and AI + Web3 community in over 30 countries and regions worldwide. It is dedicated to providing more equal and open opportunities for entrepreneurs and developers.
As the first platform in the Web3 space to achieve seamless integration between GPU computing assets and AI applications, EMC’s core products cater to various AI and Web3 application scenarios, constructing distributed high-performance computing DePIN services. For instance, EMC Hub is responsible for decentralized computing scheduling, providing global computing resources to help AI developers efficiently complete their tasks. JarvisBot focuses on a rich array of AI service applications, optimizing user experiences through deep learning and providing intelligent support for various business scenarios. OmniMuse is an innovative platform to advance the research and promotion of AI technology.
In this context, EMC is committed to fostering the construction of a decentralized AI ecosystem, offering developers low-cost and efficient computing resources while opening new possibilities for innovative applications across industries. By integrating distributed computing, smart contracts, and AI services, EMC aspires to become a significant driving force for the future integration of AI and blockchain, creating broader development opportunities for global developers and entrepreneurs.
Founding Team
Source: Edge Matrix Chain
The core team of EMC includes several industry veterans with extensive backgrounds in cloud computing, AI, and marketing:
- Alex Goh
The co-founder of EMC and Chairman of the EMC Foundation holds an MBA from Macquarie University. He has over 20 years of experience in global market development, previously serving as General Manager for Greater China at Improbable.io and Global GM at AWS (Amazon). He is currently focused on EMC’s commercialization and global promotion in Singapore.
- BK Khur
Co-founder and CTO of EMC, graduated from the College of Engineering at Nanyang Technological University (NTU) and was a researcher at NTU. He has a rich technical background, working at Deloitte Consulting on digital transformation. He co-founded companies such as JuzToday and ShopperBoard, bringing extensive management and technical experience from various innovative projects.
- Allen Foo
Board member of the EMC Foundation and product and technology advisor. He is the founder and CEO of UCCVR, an early-stage venture capital fund, and VooX. He previously led business development for Unity and Microsoft in Greater China, with significant leadership experience in the cloud services sector.
- Terrence Ngu
Board member of the EMC Foundation and global market promotion strategy advisor. He founded Hashmeta and formerly served as Chief Community Officer at StarNgage. Terrence has held key positions in several high-tech companies, focusing on global market strategy and community building.
Current Funding Situation
Currently, the EMC project has completed multiple rounds of significant financing, demonstrating its strong development potential in the global AI and Web3 sectors. The first round of financing was completed in January 2024, with major investors including Swiss Bochsler Group, Future3 Campus, 1783 Labs, Frontier Research, DMC, VOFO Corp, Exabits.ai, Hashmeta, CEEX Labs, and other institutions and family offices.
In February 2024, the EMC team announced the completion of a second round of strategic financing, led by Faculty Group and Flow Capital, amounting to several million dollars. The funding sources included the global Web3 community, DAOs, and AI developer communities, further accelerating the deployment and development of EMC’s computing nodes.
On August 30, 2024, EMC announced the successful completion of a $20 million Series A financing round led by Amber Group and P2 Ventures. Other participants included well-known investment institutions such as One Comma, Kapley Judge and Associated Corporations, and Cyberrock Venture Fund. This further strengthened EMC’s position as a decentralized computing scheduling platform and an industry innovator in AI.
Related Resources
Project Advantage
Distributed GPU
In the context of the high-performance GPU market being dominated by giants like NVIDIA, EMC effectively addresses the imbalance between supply and demand for computing power by leveraging its unique distributed decentralized computing network and utilizing idle GPU resources worldwide. Especially after Ethereum’s merge, the closure of numerous mining farms has led to many idle GPU devices, allowing EMC to offer cost-effective computing support to AI developers.
The EMC network has deployed over 100 GPU nodes across multiple countries and regions, with major models including A100, H100, RTX 4090, and 3090. These computing resources are provided by Internet Data Centers (IDC), Cloud Service Providers (CSP), mining farms, and EMC AI Workstations specifically designed for AI development. The EMC network employs a mechanism that combines Proof of Work (PoW) and Proof of Stake (PoS), allowing participants to earn token rewards by contributing computing power and staking, thus achieving dual revenue from mining and staking.
From a user experience perspective, the EMC AI Workstation is designed for plug-and-play ease of use. The first batch of products is equipped with high-performance hardware, such as Intel Core i7 CPUs, 2TB solid-state drives, 32GB DDR5 6400Hz memory, and RTX 4090 graphics cards, ensuring that the necessary computing resources and data processing capabilities for complex AI tasks are met. This provides users an efficient working environment, fostering innovation and development throughout the ecosystem.
Source: Geomap
Innovative DeAI Ecosystem
EMC has built a complete system from infrastructure to application development through its unique decentralized AI ecosystem (DeAI). Its core philosophy is openness, transparency, and democratization, aiming to address the issues of traditional centralized AI through decentralized models, data, and computing power. For example, a few companies often control traditional AI models, leading to the enclosure of data and algorithms. In EMC’s DeAI system, algorithms and data are shared through a distributed network, allowing users to manage their data autonomously, greatly enriching the data ecosystem and enhancing users’ contributions and control over AI models.
As the bull market arrives, the demand for new technologies and innovative models is particularly urgent, and the combination of AI and Web3 is a significant trend for the future market. By integrating these two hot fields, EMC has created a new market narrative, providing investors with fresh investment opportunities, especially in the decentralized AI development and application market, which is expected to trigger a new wave of investment.
Dual Token + Dual Deflation
EMC adopts a “Dual Token + Dual Deflation” model: one token is used for governance and participating in ecosystem decisions, while the other serves as the primary medium of exchange. This design enhances the project’s flexibility, allowing the tokens to play distinct roles in different functions.
Moreover, EMC’s dual deflation mechanism reduces the circulation of tokens through specific economic designs to ensure their scarcity. Specifically, this includes regular token buybacks to decrease market circulation and further reduce circulation by destroying a portion of tokens (such as transaction fees collected). This mechanism not only maintains the scarcity of the tokens but also enhances their long-term value.
In the EMC community, users can actively engage in the EMC ecosystem through various methods, such as charging tokens, participating in real-world asset (RWA) transactions, and selling AI models, thereby promoting the circulation and use of tokens. In summary, this “Dual Token + Dual Deflation” model establishes a solid economic foundation for EMC and incentivizes more developers and users to participate in the EMC ecosystem through diversified revenue models.
Source: Edge Matrix Chain
Low technical barrier
EMC has significantly lowered the technical barriers for AI DApp development by launching the EMC Hub tool. Developers can easily create and deploy AI applications using its extensive SDK and toolset. This open and user-friendly development platform attracts more developers to join the EMC ecosystem. It promotes the widespread adoption of AI technology within the Web3 ecosystem, paving the way for the rapid application of intelligent AI technologies.
EMC Ecosystem Introduction
As a project that combines artificial intelligence and Web3 technology, the entire EMC ecosystem is divided into four layers: Protocol Layer, Network Layer, Application Layer, and Asset Layer. Technically, it offers users efficient AI computing solutions through a unique network topology, edge computing scheduling, and a multi-layered node design.
Source: Edge Matrix Chain
EMC Protocol
EMC Protocol is a distributed AI computing power scheduling solution based on the EVM ecosystem. It leverages the high-performance infrastructure of the Arbitrum One main chain to enable the submission and validation of state machines. The goal is to schedule idle computing resources globally to meet the high computational demands of AI training tasks.
As shown in the diagram, the network topology of EMC can be divided into four types of nodes: Computing Nodes, Router Nodes, Validator Nodes, and Transaction Storage. These nodes, which are responsible for different functions, are uniformly scheduled to facilitate the submission and confirmation of executed transactions. They work together to complete AI model training and inference tasks. Ultimately, all transaction statuses and computing task results are stored in the Transaction Storage layer of Arbitrum One, ensuring the long-term availability of data.
Source: cryptoviet.info
EMC Protocol’s core technological implementation relies on its efficient submission and confirmation mechanism, providing unique advantages in computing power scheduling and validation node management. Firstly, the submission mechanism packages the state machine into a commitment structure sent to the Arbitrum main chain for recording, known as “submission.” At this stage, users can immediately proceed to the next step without waiting for actual confirmation. Once a transaction is submitted to the smart contract, it is considered complete, and the process is asynchronous. Although some time is required, the user’s perception of delay is significantly reduced.
Under the PoS mechanism, validation nodes fulfill their duties by staking EMC tokens to ensure fairness and reliability. The staked assets may be forfeited if validation fails, further enhancing the system’s security. The incentive mechanism is linked to the amount of staked EMC, with nodes that stake the most having priority to become validators. Smart routing also relies on staking to ensure priority allocation and task stability. Computing nodes have two options: stake EMC for higher rewards or execute tasks that do not require long-term computing power, increasing operational flexibility and profit, particularly suited for smaller nodes.
At the same time, the EMC Protocol greatly enhances computing power utilization efficiency through edge computing scheduling. Compared to traditional centralized data centers, the EMC network utilizes idle GPU resources globally, optimizing the allocation of computing capacity. Through collaboration with the EMC Partner Network (EPN), EMC achieves global decentralized computing support, ensuring the system’s stability and scalability under large-scale concurrent situations. This design enables the EMC Protocol to effectively address the challenges of today’s complex computing environments, providing a solid foundation for AI and real-time applications.
EMC Hub
The EMC HUB platform enhances development and deployment efficiency by integrating the AI model library with computing resources. Developers can package AI models into Docker containers and upload them to the platform, along with sample code and parameter descriptions, to receive rewards from the platform. This mechanism significantly reduces the burden on developers regarding model publication and distribution. Users simply need to subscribe to computing nodes and can run these model Docker containers with one-click deployment, quickly launching complete AI instances. The system will also automatically configure the corresponding APIs.
Source：EMCHub
Regarding computing power scheduling, EMC Hub relies on the collaboration of intelligent routing and nodes: the former optimizes paths and data transmission, while the latter executes computing tasks. This involves dynamically scheduling GPU resources within the computing pool and intelligently allocating them based on task load and priority. Compared to traditional methods, this model avoids the cumbersome processes of cloud service purchases, model selection, and environment deployment, allowing developers to focus more on innovation.
Regarding security and efficiency, EMC Hub employs a hybrid consensus algorithm of PoS and PoW, with a total of 3F + 1 validator nodes maintaining the mechanism. Verification is completed using a Byzantine Fault Tolerance (IBFT) algorithm that confirms transactions with a 2/3 majority. PoS ensures the security of the nodes, preventing malicious attacks, while PoW is responsible for verifying the completion of computing tasks. This hybrid mechanism enhances the platform’s security and shortens AI training cycles. Statistics indicate that this approach costs only 30% of traditional methods, reducing workloads to just a few hours.
Source: EMCprotocol (EMC) · GitHub
JarvisBot
EMC’s AI assistant Jarvis is a revolutionary AI development platform that leverages the EMC network and decentralized architecture, integrating deep learning algorithms. This makes it more than just an AI chatbot; it enhances the accuracy of computing resource allocation through deep learning while retaining strong conversational abilities. It automates complex computation tasks and model training, optimizing the AI deployment process.
In terms of functionality, JarvisBot offers a variety of AI applications, including content generation, image creation, translation, and article rewriting. Users can create customized bots for customer support, lead generation, order updates, and personalized recommendations. By incorporating a Web3 economic model, users can earn rewards by contributing resources while enjoying AI services. This sets JarvisBot apart from traditional AI applications, which typically rely on user subscriptions, truly sharing the development and creation of AI. This model has garnered significant attention in the market.
Additionally, JarvisBot is designed to simplify the deployment process of AI models greatly. With the Web3 tools provided by JarvisBot, developers can easily access its features and rapidly launch AI models without cumbersome manual configurations. This improves the efficiency of model training and provides a more effective and economical solution for AI and decentralized AI (DeAI), making it a decentralized version of “ChatGPT.”
Source: docs.jarvisbot.ai
OmniMuse
OmniMuse is an innovative platform promoting AI technology development through decentralized artificial intelligence (DeAI). It offers a range of features, including customizable smart contract templates and frameworks specifically designed for model minting, trading, and data sharing, significantly accelerating the development process of AI applications. Additionally, OmniMuse integrates popular blockchain development tools to simplify the creation of decentralized applications.
OmniMuse utilizes decentralized storage solutions like IPFS to ensure the permanence and immutability of data assets, facilitating secure data sharing and trading while prioritizing data privacy. Its advanced privacy and security features benefit from cutting-edge encryption tools, such as homomorphic encryption, secure multi-party computation, and verifiable computation, further enhancing the platform’s security.
Furthermore, the DeAI Store under development will be an innovative platform gathering decentralized AI applications, helping users discover and access the latest intelligent technology applications. The DeAI Store offers decentralized AI data storage, smart contract templates, and development frameworks while integrating encryption tools to ensure user privacy and security. The platform aims to create a collaborative environment without technical “boundaries,” allowing everyone to share to unleash the tremendous potential of AI and attract numerous AI developers, creators, and users to drive innovation and development in AI technology jointly.
Source: OmniMuse
Openverse Framework
Based on EMC Hub, Openverse further expands its functionality by integrating multiple developer tools and SDKs. This enhances developers’ capabilities in a decentralized environment and facilitates seamless integration with EMC Hub, allowing developers to deploy AI applications quickly.
Functionally, Openverse is a platform that integrates various SDK tools for Web3 developers, including EMC SDK, Web3 SDK, 3D Scene SDK, and DID SDK. These tools support core Web3 functionalities; for instance, the 3D Scene SDK enables rapid construction of virtual 3D worlds, while the DID SDK provides blockchain-level identity verification to ensure data privacy and security.
Developers can upload AI models to the platform and easily launch and manage AI instances through Openverse’s one-click deployment feature, simplifying the development process. This integrated platform significantly lowers the barriers to Web3 development, enabling developers to focus on application innovation and business growth.
Source: EMCprotocol (EMC) · GitHub
Overview of Related Tokens.
Token Distribution
$EMC is a token issued on the Arbitrum One public chain, with a total supply of 1 billion. The distribution of these tokens covers various purposes, including community rewards, development funds, and liquidity. Its design aims to allow developers and users to participate in the decentralized computing ecosystem, incentivizing active engagement in the ecosystem’s development to achieve efficient utilization of computing power and economic circulation.
Source: Token Distribution | EMC Whitepaper
Yield deflation model
EMC introduces a dual-token economic model, featuring the basic $EMC token and a stablecoin called Credits, which serves as the medium for transactions within the EMC market. The core of this mechanism lies in the requirement for users to purchase Credits using $EMC, resulting in the complete destruction of $EMC during this process, thereby enhancing its scarcity and value. This design helps maintain the stable growth of $EMC’s price and attracts more users to the EMC ecosystem.
EMC’s yield deflation model consists of specific yield deflation and computing power consumption deflation, which aim to maintain the balance of token supply and demand.
- In the yield deflation mechanism, transactions utilize stablecoins or other tokens, while computing power clusters are leased through the computing power protocol. Transaction fees on the RWA platform are converted into $EMC and destroyed, with 80% of the rental income going to the lessor and 20% being destroyed. Additionally, both parties in the transaction will receive $EMC as a reward, further promoting the use and destruction of the token.
- In the computing power consumption deflation mechanism, EMC Hub provides a rich array of computing power and application subscription services, requiring users to stake other cryptocurrencies to access these services. In this case, the staked assets are converted into $EMC, which cannot be withdrawn from the consumption account after being deposited but is destroyed after the service is utilized, increasing the consumption of $EMC and enhancing its value.
Release model
The initial token generation event (TGE) for the EMC token will start on November 9, 2023, with the entire token release plan lasting 24 to 48 months, covering investors and the project team. In token distribution, ecosystem rewards (including governance tokens) account for 47% of the total supply. Additionally, the EMC economic system incorporates deflationary mechanisms and a burning plan aimed at operating the ecosystem and enhancing the long-term value of the token.
Source: Token Acquisition | EMC Whitepaper
Future Value Analysis
The EMC project is a combination of traditional Web2 and Web3. Compared to Web2 projects, its advantage lies in leveraging distributed GPU nodes to effectively aggregate dispersed computing resources, alleviating the supply-demand imbalance caused by traditional centralized systems. In contrast to other Web3 projects, EMC offers a cost-effective solution for AI model training by deeply integrating AI with DePIN, establishing a market for sharing knowledge, data, and computing assets. Additionally, its unique Credits mechanism accelerates economic circulation, providing new revenue streams and opportunities for investors.
In terms of future applications, EMC makes high-performance computing accessible and economical, opening the door to AI applications across various industries.
For example, in the healthcare sector, EMC can utilize its powerful computing capabilities to process large-scale medical data, advancing personalized medicine and precise diagnostics. AI models can formulate more effective treatment plans by analyzing patients’ historical data and genetic information. EMC’s computing power in the financial industry can handle complex financial transactions and risk assessments, reducing costs while ensuring data security and transparency.
The most promising applications focus on smart cities and the Internet of Things (IoT). EMC’s distributed architecture can support real-time data processing for numerous devices, facilitating optimizing systems like intelligent transportation and energy management, thereby enhancing urban operational efficiency and sustainability.
Currently, the engineering technology for large models is relatively mature, but the stability of computing power and the reliability of code encapsulation still require close attention and continuous optimization. Given that the EMC project is in a hot segment of DePIN, it initially possesses feasibility in customer experience (CX). On the other hand, based on the disclosed information, the project’s Chinese background is evident, suggesting that future market expansion may require a diversification strategy to enhance global influence.
References
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EMC: A New Force Driving the Integration of AI Technology and Blockchain
Introduction
What is EMC?
Project Advantage
EMC Ecosystem Introduction
Overview of Related Tokens.
Future Value Analysis
Introduction
Integrating artificial intelligence and blockchain technology is becoming a new focal point in the rapidly evolving wave of technology. The narrative of building a computing power DePin based on Graphics Processing Units (GPUs) is beginning to create a new wave in the Web3 space.
In recent years, the widespread application of AI technology has led to a growing demand for computing power resources across various industries. However, the monopoly of high-performance GPUs in the market has made it difficult for many small and medium-sized enterprises to obtain the necessary computing support. Based on this demand trend, the EMC (Edge Matrix Computing) project was born, aiming to solve the problem of insufficient computing power allocation by integrating idle graphics card resources from around the world.
The EMC team has pioneered the “DeAI” concept, distinguishing it from traditional GPU cloud services. The project provides an efficient AI training model through its computing power scheduling platform, which enables developers to access computing resources at low costs. This innovation promotes the integration of AI and blockchain in resource utilization and data sharing, empowering the development of the Web3 ecosystem and creating real application value.
What is EMC?
Project Overview
EMC (Edge Matrix Computing) was established in 2022 as a high-performance decentralized AI computing application network, aiming to address the friction between the development of AI technology and GPU computing power resources. As of October 2024, it has built a computing power network and AI + Web3 community in over 30 countries and regions worldwide. It is dedicated to providing more equal and open opportunities for entrepreneurs and developers.
As the first platform in the Web3 space to achieve seamless integration between GPU computing assets and AI applications, EMC’s core products cater to various AI and Web3 application scenarios, constructing distributed high-performance computing DePIN services. For instance, EMC Hub is responsible for decentralized computing scheduling, providing global computing resources to help AI developers efficiently complete their tasks. JarvisBot focuses on a rich array of AI service applications, optimizing user experiences through deep learning and providing intelligent support for various business scenarios. OmniMuse is an innovative platform to advance the research and promotion of AI technology.
In this context, EMC is committed to fostering the construction of a decentralized AI ecosystem, offering developers low-cost and efficient computing resources while opening new possibilities for innovative applications across industries. By integrating distributed computing, smart contracts, and AI services, EMC aspires to become a significant driving force for the future integration of AI and blockchain, creating broader development opportunities for global developers and entrepreneurs.
Founding Team
Source: Edge Matrix Chain
The core team of EMC includes several industry veterans with extensive backgrounds in cloud computing, AI, and marketing:
- Alex Goh
The co-founder of EMC and Chairman of the EMC Foundation holds an MBA from Macquarie University. He has over 20 years of experience in global market development, previously serving as General Manager for Greater China at Improbable.io and Global GM at AWS (Amazon). He is currently focused on EMC’s commercialization and global promotion in Singapore.
- BK Khur
Co-founder and CTO of EMC, graduated from the College of Engineering at Nanyang Technological University (NTU) and was a researcher at NTU. He has a rich technical background, working at Deloitte Consulting on digital transformation. He co-founded companies such as JuzToday and ShopperBoard, bringing extensive management and technical experience from various innovative projects.
- Allen Foo
Board member of the EMC Foundation and product and technology advisor. He is the founder and CEO of UCCVR, an early-stage venture capital fund, and VooX. He previously led business development for Unity and Microsoft in Greater China, with significant leadership experience in the cloud services sector.
- Terrence Ngu
Board member of the EMC Foundation and global market promotion strategy advisor. He founded Hashmeta and formerly served as Chief Community Officer at StarNgage. Terrence has held key positions in several high-tech companies, focusing on global market strategy and community building.
Current Funding Situation
Currently, the EMC project has completed multiple rounds of significant financing, demonstrating its strong development potential in the global AI and Web3 sectors. The first round of financing was completed in January 2024, with major investors including Swiss Bochsler Group, Future3 Campus, 1783 Labs, Frontier Research, DMC, VOFO Corp, Exabits.ai, Hashmeta, CEEX Labs, and other institutions and family offices.
In February 2024, the EMC team announced the completion of a second round of strategic financing, led by Faculty Group and Flow Capital, amounting to several million dollars. The funding sources included the global Web3 community, DAOs, and AI developer communities, further accelerating the deployment and development of EMC’s computing nodes.
On August 30, 2024, EMC announced the successful completion of a $20 million Series A financing round led by Amber Group and P2 Ventures. Other participants included well-known investment institutions such as One Comma, Kapley Judge and Associated Corporations, and Cyberrock Venture Fund. This further strengthened EMC’s position as a decentralized computing scheduling platform and an industry innovator in AI.
Related Resources
Project Advantage
Distributed GPU
In the context of the high-performance GPU market being dominated by giants like NVIDIA, EMC effectively addresses the imbalance between supply and demand for computing power by leveraging its unique distributed decentralized computing network and utilizing idle GPU resources worldwide. Especially after Ethereum’s merge, the closure of numerous mining farms has led to many idle GPU devices, allowing EMC to offer cost-effective computing support to AI developers.
The EMC network has deployed over 100 GPU nodes across multiple countries and regions, with major models including A100, H100, RTX 4090, and 3090. These computing resources are provided by Internet Data Centers (IDC), Cloud Service Providers (CSP), mining farms, and EMC AI Workstations specifically designed for AI development. The EMC network employs a mechanism that combines Proof of Work (PoW) and Proof of Stake (PoS), allowing participants to earn token rewards by contributing computing power and staking, thus achieving dual revenue from mining and staking.
From a user experience perspective, the EMC AI Workstation is designed for plug-and-play ease of use. The first batch of products is equipped with high-performance hardware, such as Intel Core i7 CPUs, 2TB solid-state drives, 32GB DDR5 6400Hz memory, and RTX 4090 graphics cards, ensuring that the necessary computing resources and data processing capabilities for complex AI tasks are met. This provides users an efficient working environment, fostering innovation and development throughout the ecosystem.
Source: Geomap
Innovative DeAI Ecosystem
EMC has built a complete system from infrastructure to application development through its unique decentralized AI ecosystem (DeAI). Its core philosophy is openness, transparency, and democratization, aiming to address the issues of traditional centralized AI through decentralized models, data, and computing power. For example, a few companies often control traditional AI models, leading to the enclosure of data and algorithms. In EMC’s DeAI system, algorithms and data are shared through a distributed network, allowing users to manage their data autonomously, greatly enriching the data ecosystem and enhancing users’ contributions and control over AI models.
As the bull market arrives, the demand for new technologies and innovative models is particularly urgent, and the combination of AI and Web3 is a significant trend for the future market. By integrating these two hot fields, EMC has created a new market narrative, providing investors with fresh investment opportunities, especially in the decentralized AI development and application market, which is expected to trigger a new wave of investment.
Dual Token + Dual Deflation
EMC adopts a “Dual Token + Dual Deflation” model: one token is used for governance and participating in ecosystem decisions, while the other serves as the primary medium of exchange. This design enhances the project’s flexibility, allowing the tokens to play distinct roles in different functions.
Moreover, EMC’s dual deflation mechanism reduces the circulation of tokens through specific economic designs to ensure their scarcity. Specifically, this includes regular token buybacks to decrease market circulation and further reduce circulation by destroying a portion of tokens (such as transaction fees collected). This mechanism not only maintains the scarcity of the tokens but also enhances their long-term value.
In the EMC community, users can actively engage in the EMC ecosystem through various methods, such as charging tokens, participating in real-world asset (RWA) transactions, and selling AI models, thereby promoting the circulation and use of tokens. In summary, this “Dual Token + Dual Deflation” model establishes a solid economic foundation for EMC and incentivizes more developers and users to participate in the EMC ecosystem through diversified revenue models.
Source: Edge Matrix Chain
Low technical barrier
EMC has significantly lowered the technical barriers for AI DApp development by launching the EMC Hub tool. Developers can easily create and deploy AI applications using its extensive SDK and toolset. This open and user-friendly development platform attracts more developers to join the EMC ecosystem. It promotes the widespread adoption of AI technology within the Web3 ecosystem, paving the way for the rapid application of intelligent AI technologies.
EMC Ecosystem Introduction
As a project that combines artificial intelligence and Web3 technology, the entire EMC ecosystem is divided into four layers: Protocol Layer, Network Layer, Application Layer, and Asset Layer. Technically, it offers users efficient AI computing solutions through a unique network topology, edge computing scheduling, and a multi-layered node design.
Source: Edge Matrix Chain
EMC Protocol
EMC Protocol is a distributed AI computing power scheduling solution based on the EVM ecosystem. It leverages the high-performance infrastructure of the Arbitrum One main chain to enable the submission and validation of state machines. The goal is to schedule idle computing resources globally to meet the high computational demands of AI training tasks.
As shown in the diagram, the network topology of EMC can be divided into four types of nodes: Computing Nodes, Router Nodes, Validator Nodes, and Transaction Storage. These nodes, which are responsible for different functions, are uniformly scheduled to facilitate the submission and confirmation of executed transactions. They work together to complete AI model training and inference tasks. Ultimately, all transaction statuses and computing task results are stored in the Transaction Storage layer of Arbitrum One, ensuring the long-term availability of data.
Source: cryptoviet.info
EMC Protocol’s core technological implementation relies on its efficient submission and confirmation mechanism, providing unique advantages in computing power scheduling and validation node management. Firstly, the submission mechanism packages the state machine into a commitment structure sent to the Arbitrum main chain for recording, known as “submission.” At this stage, users can immediately proceed to the next step without waiting for actual confirmation. Once a transaction is submitted to the smart contract, it is considered complete, and the process is asynchronous. Although some time is required, the user’s perception of delay is significantly reduced.
Under the PoS mechanism, validation nodes fulfill their duties by staking EMC tokens to ensure fairness and reliability. The staked assets may be forfeited if validation fails, further enhancing the system’s security. The incentive mechanism is linked to the amount of staked EMC, with nodes that stake the most having priority to become validators. Smart routing also relies on staking to ensure priority allocation and task stability. Computing nodes have two options: stake EMC for higher rewards or execute tasks that do not require long-term computing power, increasing operational flexibility and profit, particularly suited for smaller nodes.
At the same time, the EMC Protocol greatly enhances computing power utilization efficiency through edge computing scheduling. Compared to traditional centralized data centers, the EMC network utilizes idle GPU resources globally, optimizing the allocation of computing capacity. Through collaboration with the EMC Partner Network (EPN), EMC achieves global decentralized computing support, ensuring the system’s stability and scalability under large-scale concurrent situations. This design enables the EMC Protocol to effectively address the challenges of today’s complex computing environments, providing a solid foundation for AI and real-time applications.
EMC Hub
The EMC HUB platform enhances development and deployment efficiency by integrating the AI model library with computing resources. Developers can package AI models into Docker containers and upload them to the platform, along with sample code and parameter descriptions, to receive rewards from the platform. This mechanism significantly reduces the burden on developers regarding model publication and distribution. Users simply need to subscribe to computing nodes and can run these model Docker containers with one-click deployment, quickly launching complete AI instances. The system will also automatically configure the corresponding APIs.
Source：EMCHub
Regarding computing power scheduling, EMC Hub relies on the collaboration of intelligent routing and nodes: the former optimizes paths and data transmission, while the latter executes computing tasks. This involves dynamically scheduling GPU resources within the computing pool and intelligently allocating them based on task load and priority. Compared to traditional methods, this model avoids the cumbersome processes of cloud service purchases, model selection, and environment deployment, allowing developers to focus more on innovation.
Regarding security and efficiency, EMC Hub employs a hybrid consensus algorithm of PoS and PoW, with a total of 3F + 1 validator nodes maintaining the mechanism. Verification is completed using a Byzantine Fault Tolerance (IBFT) algorithm that confirms transactions with a 2/3 majority. PoS ensures the security of the nodes, preventing malicious attacks, while PoW is responsible for verifying the completion of computing tasks. This hybrid mechanism enhances the platform’s security and shortens AI training cycles. Statistics indicate that this approach costs only 30% of traditional methods, reducing workloads to just a few hours.
Source: EMCprotocol (EMC) · GitHub
JarvisBot
EMC’s AI assistant Jarvis is a revolutionary AI development platform that leverages the EMC network and decentralized architecture, integrating deep learning algorithms. This makes it more than just an AI chatbot; it enhances the accuracy of computing resource allocation through deep learning while retaining strong conversational abilities. It automates complex computation tasks and model training, optimizing the AI deployment process.
In terms of functionality, JarvisBot offers a variety of AI applications, including content generation, image creation, translation, and article rewriting. Users can create customized bots for customer support, lead generation, order updates, and personalized recommendations. By incorporating a Web3 economic model, users can earn rewards by contributing resources while enjoying AI services. This sets JarvisBot apart from traditional AI applications, which typically rely on user subscriptions, truly sharing the development and creation of AI. This model has garnered significant attention in the market.
Additionally, JarvisBot is designed to simplify the deployment process of AI models greatly. With the Web3 tools provided by JarvisBot, developers can easily access its features and rapidly launch AI models without cumbersome manual configurations. This improves the efficiency of model training and provides a more effective and economical solution for AI and decentralized AI (DeAI), making it a decentralized version of “ChatGPT.”
Source: docs.jarvisbot.ai
OmniMuse
OmniMuse is an innovative platform promoting AI technology development through decentralized artificial intelligence (DeAI). It offers a range of features, including customizable smart contract templates and frameworks specifically designed for model minting, trading, and data sharing, significantly accelerating the development process of AI applications. Additionally, OmniMuse integrates popular blockchain development tools to simplify the creation of decentralized applications.
OmniMuse utilizes decentralized storage solutions like IPFS to ensure the permanence and immutability of data assets, facilitating secure data sharing and trading while prioritizing data privacy. Its advanced privacy and security features benefit from cutting-edge encryption tools, such as homomorphic encryption, secure multi-party computation, and verifiable computation, further enhancing the platform’s security.
Furthermore, the DeAI Store under development will be an innovative platform gathering decentralized AI applications, helping users discover and access the latest intelligent technology applications. The DeAI Store offers decentralized AI data storage, smart contract templates, and development frameworks while integrating encryption tools to ensure user privacy and security. The platform aims to create a collaborative environment without technical “boundaries,” allowing everyone to share to unleash the tremendous potential of AI and attract numerous AI developers, creators, and users to drive innovation and development in AI technology jointly.
Source: OmniMuse
Openverse Framework
Based on EMC Hub, Openverse further expands its functionality by integrating multiple developer tools and SDKs. This enhances developers’ capabilities in a decentralized environment and facilitates seamless integration with EMC Hub, allowing developers to deploy AI applications quickly.
Functionally, Openverse is a platform that integrates various SDK tools for Web3 developers, including EMC SDK, Web3 SDK, 3D Scene SDK, and DID SDK. These tools support core Web3 functionalities; for instance, the 3D Scene SDK enables rapid construction of virtual 3D worlds, while the DID SDK provides blockchain-level identity verification to ensure data privacy and security.
Developers can upload AI models to the platform and easily launch and manage AI instances through Openverse’s one-click deployment feature, simplifying the development process. This integrated platform significantly lowers the barriers to Web3 development, enabling developers to focus on application innovation and business growth.
Source: EMCprotocol (EMC) · GitHub
Overview of Related Tokens.
Token Distribution
$EMC is a token issued on the Arbitrum One public chain, with a total supply of 1 billion. The distribution of these tokens covers various purposes, including community rewards, development funds, and liquidity. Its design aims to allow developers and users to participate in the decentralized computing ecosystem, incentivizing active engagement in the ecosystem’s development to achieve efficient utilization of computing power and economic circulation.
Source: Token Distribution | EMC Whitepaper
Yield deflation model
EMC introduces a dual-token economic model, featuring the basic $EMC token and a stablecoin called Credits, which serves as the medium for transactions within the EMC market. The core of this mechanism lies in the requirement for users to purchase Credits using $EMC, resulting in the complete destruction of $EMC during this process, thereby enhancing its scarcity and value. This design helps maintain the stable growth of $EMC’s price and attracts more users to the EMC ecosystem.
EMC’s yield deflation model consists of specific yield deflation and computing power consumption deflation, which aim to maintain the balance of token supply and demand.
- In the yield deflation mechanism, transactions utilize stablecoins or other tokens, while computing power clusters are leased through the computing power protocol. Transaction fees on the RWA platform are converted into $EMC and destroyed, with 80% of the rental income going to the lessor and 20% being destroyed. Additionally, both parties in the transaction will receive $EMC as a reward, further promoting the use and destruction of the token.
- In the computing power consumption deflation mechanism, EMC Hub provides a rich array of computing power and application subscription services, requiring users to stake other cryptocurrencies to access these services. In this case, the staked assets are converted into $EMC, which cannot be withdrawn from the consumption account after being deposited but is destroyed after the service is utilized, increasing the consumption of $EMC and enhancing its value.
Release model
The initial token generation event (TGE) for the EMC token will start on November 9, 2023, with the entire token release plan lasting 24 to 48 months, covering investors and the project team. In token distribution, ecosystem rewards (including governance tokens) account for 47% of the total supply. Additionally, the EMC economic system incorporates deflationary mechanisms and a burning plan aimed at operating the ecosystem and enhancing the long-term value of the token.
Source: Token Acquisition | EMC Whitepaper
Future Value Analysis
The EMC project is a combination of traditional Web2 and Web3. Compared to Web2 projects, its advantage lies in leveraging distributed GPU nodes to effectively aggregate dispersed computing resources, alleviating the supply-demand imbalance caused by traditional centralized systems. In contrast to other Web3 projects, EMC offers a cost-effective solution for AI model training by deeply integrating AI with DePIN, establishing a market for sharing knowledge, data, and computing assets. Additionally, its unique Credits mechanism accelerates economic circulation, providing new revenue streams and opportunities for investors.
In terms of future applications, EMC makes high-performance computing accessible and economical, opening the door to AI applications across various industries.
For example, in the healthcare sector, EMC can utilize its powerful computing capabilities to process large-scale medical data, advancing personalized medicine and precise diagnostics. AI models can formulate more effective treatment plans by analyzing patients’ historical data and genetic information. EMC’s computing power in the financial industry can handle complex financial transactions and risk assessments, reducing costs while ensuring data security and transparency.
The most promising applications focus on smart cities and the Internet of Things (IoT). EMC’s distributed architecture can support real-time data processing for numerous devices, facilitating optimizing systems like intelligent transportation and energy management, thereby enhancing urban operational efficiency and sustainability.
Currently, the engineering technology for large models is relatively mature, but the stability of computing power and the reliability of code encapsulation still require close attention and continuous optimization. Given that the EMC project is in a hot segment of DePIN, it initially possesses feasibility in customer experience (CX). On the other hand, based on the disclosed information, the project’s Chinese background is evident, suggesting that future market expansion may require a diversification strategy to enhance global influence.
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