DePIN (Decentralized Physical Infrastructure Network) aims to build a distributed network infrastructure. It involves selecting platforms that suit specific needs and addressing challenges such as scalability and cost-effectiveness. DePIN aims to provide customized solutions for different application scenarios to meet their specific requirements and promote the development of the entire ecosystem. ZKML (Zero-Knowledge Marking Language) is designed to protect the privacy of AI models and input data as well as verify the correctness of the inference process. By putting the model or inference proof on the chain, ZKML allows the blockchain to perceive the physical world, and enable smart contracts to run AI models and make decisions while protecting data privacy.

The combination of ZKML and DePIN has significant potential applications in decentralized networks. By addressing cost and economies of scale issues and effectively utilizing idle resources, the combination of DePIN and ZKML brings new opportunities to emerging fields such as the Internet of Things and edge computing. The innovative integration provides robust support for the continued development of decentralized networks.

This article published by Bing Ventures explores the intersection between DePIN and ZKML by studying them together, providing new insights for more niche application scenarios. This research approach deepens our understanding of the relevance of these two fields, reveals collaborative opportunities between them, and offers valuable insights for future growth.

Challenges for Seleccting Platforms

Both DePIN projects and ZKML face important challenges in selecting platforms. DePIN-type projects, as distributed network infrastructure projects, need to carefully consider choosing the platform that best suits their needs. The options include different blockchains such as Solana, Polygon, Cosmos, Polkadot, and others, with each platform offering its own unique advantages. Similarly, the development of ZKML also requires the selection of a suitable platform to support the on-chain and verification process of AI models. Both areas require a combination of factors such as platform scalability, customization requirements and cost-effectiveness to make informed decisions.

Scalability is a common issue that both DePIN-like projects and ZKML-like projects need to address. As the DePIN project grows, scalability will become a key challenge. Traditional blockchain platforms face throughput limitations and high fees, so some projects have chosen customized platforms for specific applications or industries, such as Cosmos and Polkadot’s Appchains, as well as industry-specific platforms such as IOTEX. Similarly, ZKML needs to support the conversion of large-scale parameter neural networks into ZK circuits and improve proof efficiency to meet its scalability needs. Both areas need to find customized solutions to meet growing demands.

Cost-effectiveness is another issue that the DePIN project and the ZKML project need to share. Among DePIN projects, projects with higher storage requirements need to comprehensively consider the cost-effectiveness of different platforms to make decisions. For example, Solana has attracted some projects to migrate due to its state compression, reduced costs, and low storage requirements. For ZKML, choosing a suitable platform to implement the on-chain and verification process of AI models also requires consideration of cost-effectiveness and other factors. Both areas require focus on optimal utilization of resources to reduce project operating costs and improve economic efficiency.

By sharing experiences and solutions, the DePIN project and ZKML can learn from each other and integrate with each other, creating more inspiration and opportunities. The DePIN project can learn from ZKML’s experience in selecting appropriate platforms to understand the suitability of each platform for the AI ​​model on-chain and verification process. At the same time, the customized solutions of the DePIN project may provide ideas for ZKML to meet the on-chain requirements of large-scale parameter neural networks.

Source: Bing Ventures

Integration of ZKML and DePIN

The combination of ZKML and DePIN has the potential to drive the development of decentralized networks, addressing issues such as cost, scalability, and effective utilization of idle resources, as well as exploring potential applications in emerging fields. Projects like DePIN, using blockchain technology and token incentive models similar to Filecoin, Arweave, Helium, and Render Network, can offer storage, network signal, and rendering services at low prices, achieving cost reduction and rapid scalability. Compared to the heavy asset models in traditional ICT industries, DePIN’s innovative model effectively reduces labor costs, factory costs, and operational costs.

Beyond addressing cost issues, the combination of DePIN and ZKML can pioneer a sharing economy model and make efficient use of various idle resources. In addition to resources already utilized, such as hard drive storage, communication bandwidth, and GPU computing power, DePIN has the potential to leverage more types of idle resources, such as cameras, screens, and cognitive abilities. By centrally utilizing these resources to provide services for businesses, DePIN creates additional markets and value in the digital information field, offering individuals opportunities for additional income.

The combination of ZKML and DePIN has the potential to drive the development of decentralized networks, addressing issues such as cost, scalability, and effective utilization of idle resources, as well as exploring potential applications in emerging fields. Projects like DePIN, using blockchain technology and token incentive models similar to Filecoin, Arweave, Helium, and Render Network, can offer storage, network signal, and rendering services at low prices, achieving cost reduction and rapid scalability. Compared to the heavy asset models in traditional ICT industries, DePIN’s innovative model effectively reduces labor costs, factory costs, and operational costs.

Beyond addressing cost issues, the combination of DePIN and ZKML can pioneer a sharing economy model and make efficient use of various idle resources. In addition to resources already utilized, such as hard drive storage, communication bandwidth, and GPU computing power, DePIN has the potential to leverage more types of idle resources, such as cameras, screens, and cognitive abilities. By centrally utilizing these resources to provide services for businesses, DePIN creates additional markets and value in the digital information field, offering individuals opportunities for additional income.

In emerging fields, the combination of DePIN and ZKML presents significant application potential. For instance, in the Internet of Things (IoT) domain, DePIN can utilize decentralized hardware resources to offer more secure and privacy-protected smart device connectivity and data processing services. Through the use of ZKML technology to verify data transfer and sharing between devices, DePIN ensures the security and privacy of user data, promoting the growth of smart contracts capable of making autonomous, flexible decisions based on real-time data.

Furthermore, the combination of DePIN and ZKML can be applied in the field of edge computing, providing more opportunities and development space. By moving ML model inference and data processing to edge devices, DePIN can offer low-latency, high-efficiency edge computing services. Coupled with ZKML technology, DePIN can ensure data privacy while verifying the accuracy and trustworthiness of inference results on edge devices. This application has the potential to be widely used in areas such as the Internet of Things, smart cities, and industrial automation.

Source: 1k(x)

Confronting instability

First, the hardware network for token incentive management face instability and security risks. Fluctuations in token prices may lead to imbalances in network balances, thereby affecting the stability of the network. In addition, node misoperation may cause failures, and malicious nodes and hacker attacks are also potential risks. Therefore, the project team needs to have a high degree of business capabilities and further improve the overall blockchain infrastructure to solve these problems.

Secondly, the idle resource reuse model raises ethical issues. Although it is feasible to utilize idle resources during non-normal usage hours, whether we should pursue keeping all hardware at full capacity continuously is a question that requires in-depth consideration. The core of the sharing economic model is trust, and errors in token incentive design may be exploited by a few people, causing losses to most participants and users. In addition, the protection of personal data and privacy is also an important ethical issue that requires full attention and protection during technology implementation.

Solving these issues requires a collaborative effort among the project team, technical community, and relevant stakeholders. Through continuous technical improvements, the stability and security of the network can be improved. At the same time, security risk management measures, such as node monitoring and automated maintenance, as well as the detection and processing of malicious behaviors, are strengthened to ensure the stable operation of the network. In terms of ethical issues, transparent policies and rules need to be developed to ensure that users have adequate control over their personal data and privacy.

In addition, paying attention to ethical issues and formulating a reasonable regulatory framework can provide better guidance and guarantee for technology implementation. By working together to gradually solve the difficulties in the integration of ZKML and DePIN, we can ensure its steady development in practice and create more value for this field.

Source: Bing Ventures

Future potential

The combination of ZKML and DePIN demonstrates immense future potential, and we see promising investment directions to reap returns in this field.

Firstly, in terms of data storage, retrieval, and archiving, decentralized storage nodes similar to Filecoin and Arweave can optimize solutions, providing more secure, reliable, and persistent data storage. This offers investors opportunities to participate in the data storage and archiving market.

Secondly, ZKML can enhance the efficiency of Layer 3 expansion by renting external communication networks or GPU networks. This provides investors with opportunities to participate in network expansion and address performance bottlenecks, especially amid the growing demand for blockchain and decentralized applications.

Thirdly, by establishing new blockchain-based businesses in areas such as the Internet of Things (IoT), cloud computing, energy storage, and transportation data, investors can find innovative and value-added opportunities. These projects can leverage existing hardware infrastructure and blockchain network connectivity to offer more efficient, secure, and transparent services.

Fourthly, the field of customized devices has vast growing prospects. Through the integration of various devices and sensors, utilizing hardware facilities such as drone photography, spatial weather stations, and car IoT information systems, investors can enjoy more innovations and increased utility.

Lastly, specific sector services represent another important investment direction. The demand in areas including the Internet of Things, unlimited communication, GPU rendering, and video transcoding is continually growing, and the application of ZKML can meet these needs. By aligning investments with real-life demands, investors can engage and drive the advancement of these specific sectors.

In conclusion, the future potential of the integration of ZKML and DePIN is exciting. Investors can focus on data storage, network expansion, new business development, customized devices, and services in specific sectors to gain returns in the ongoing development of this field.

Source: Bing Ventures

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