Overview
What is Lilypad?
Lilypad serves as a verifiable, trustless, and decentralized computational network engineered to facilitate mainstream adoption of web3 applications. By extending unrestricted, global access to computational power, Lilypad strategically collaborates with decentralized infrastructure networks, such as Filecoin, to formulate a transparent, efficient, and accessible computational ecosystem. While Lilypad does not specifically resolve issues related to the accessibility of AI models, it significantly alleviates challenges associated with procuring high-performance AI hardware. In this context, Lilypad provides decentralized AI computational services. The network recently unveiled its second version, dubbed Lilypad V2 (Aurora), and is actively laying groundwork for multi-chain integration and the deployment of an incentivized test net.
Objective and problem statement
Lilypad aims to mitigate the challenges predominantly associated with the accessibility of high-performance computational hardware. At present, numerous barriers impede developers and organizations from seamlessly integrating projects that require high-performance computing, such as AI technologies, into their applications. Unlike conventional centralized systems, where access to powerful compute hardware is restricted and costly, Lilypad endeavors to democratize this access. Through its verifiable, trustless, and decentralized computational network, Lilypad extends unrestricted, global access to computational power. By leveraging decentralized infrastructure networks such as Filecoin, Lilypad is strategically positioned to enhance the efficiency, transparency, and accessibility of high-performance computing hardware.
Applications
Perform off-chain decentralised compute over data, with on-chain guarantees, and to call this functionality directly from a smart contract, CLI and an easy to use abstraction layer, opens the door to a multitude of possible applications including:
Inference AI jobs
ML training jobs
Invoking & supporting generic ZK computations
Cross-chain interoperability complement to bridge protocols
Utilising inbuilt storage on IPFS
Federated Learning consensus (with Bacalhau insulated jobs)
IOT & Sensor Data integrations
Providing a platform for Digital twins
Supply chain tracking & analysis
ETL & data preparation jobs
Key features
Some of the key features of Lilypad include:
Verifiable Trustless Decentralized Compute Network: Lilypad is a decentralized compute network that aims to provide global, permissionless access to compute power. It leverages decentralized physical infrastructure networks like Filecoin to ensure trustlessness and verifiability.
Mainstream Web3 Application Support: Lilypad is designed to enable mainstream web3 applications to use its compute network. It aims to make decentralized AI more accessible, efficient, and transparent for developers and users.
Open Compute Network: Lilypad creates an open compute network that allows users to access and run AI models and jobs. It separates module creators from users and curates a set of deterministic modules for users to run, ensuring determinism in verification systems.
Multichain Support: Lilypad plans to go multichain, which means it will support multiple blockchain networks. This will increase the scalability and interoperability of the network, allowing users to choose the blockchain that best suits their needs.
Incentivized Test Net: Lilypad has plans to launch an incentivized test net, which will provide users with incentives to participate in testing and improving the network. This will help identify and address any issues or challenges before the mainnet launch.
Decentralization of Mediators: The team also aims to decentralize the mediators in the network. This means that the decision-making process and governance of the network will be distributed among multiple participants, ensuring a more decentralized and resilient system.
Presentation
In the video above, Ally Haire, Co-Founder of Lilypad, will provide an introduction to the project and its goals.
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