Modular AI Support

The FLOPS chain is designed to provide a flexible and efficient infrastructure for AI projects through modular design.

Separation of Consensus and Execution

FLOPS adopts a modular architecture that separates the consensus layer from the execution layer. The consensus layer employs a hybrid POW+POS consensus mechanism, providing high security and decentralization support. The execution layer supports multiple virtual machines (VMs), including EVM and WASM, allowing developers to choose the appropriate execution environment based on the needs of their AI projects. This structure enables flexible upgrades to the execution environment without compromising overall network security.

Data Availability

The data availability layer in FLOPS ensures that all data required for transaction verification is accessible and verifiable within the network. Through data sharding and encoding techniques, FLOPS achieves efficient data storage and retrieval mechanisms while supporting light client verification, ensuring that even resource-constrained devices can participate in validation. This design not only enhances system efficiency but also improves data security and reliability.

Interoperability and Scalability

FLOPS's multi-chain architecture supports the independent operation and expansion of multiple sub-chains. By implementing cross-chain communication protocols and bridging technologies, FLOPS enables interoperability and resource sharing between different sub-chains. Each sub-chain can be customized according to specific AI application scenarios, avoiding resource contention and bottlenecks, thus ensuring flexible scalability.

Customizable Runtime Environments

FLOPS offers a variety of customizable runtime environments, supporting multiple programming languages and AI frameworks. The plug-in design allows FLOPS to dynamically load and unload different modules, while also supporting various hardware accelerators (such as GPUs and TPUs). This flexibility enables developers to select the optimal tech stack based on specific project needs, maximizing hardware resource utilization and improving computational efficiency.

Optimized for Specialized Workloads

FLOPS is optimized for specific AI workloads. For model training, FLOPS provides distributed training support and efficient resource scheduling; for model inference, FLOPS offers edge computing support and low-latency inference services. These optimizations enable FLOPS to fully utilize hardware resources, providing high-performance support for AI applications.

Governance and Upgradability

FLOPS introduces an on-chain governance mechanism, allowing community members to participate in network decisions through proposals and voting. FLOPS employs a decentralized autonomous organization (DAO) structure, ensuring all governance decisions are transparent and traceable. Additionally, FLOPS supports modular upgrades, allowing for independent updates of individual modules, ensuring smooth network transitions and sustained development.

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