Reddio's White Paper Highlights Integration of AI with Parallel EVM

According to PANews, Reddio has released a white paper detailing the integration of automated AI execution within the Ethereum Virtual Machine (EVM) ecosystem. This development aims to fill a gap in the Ethereum ecosystem's AI capabilities. The concept of a parallel EVM has been seen as a crucial step in bridging the technological gap between the traditional EVM ecosystem and high-performance chains like Solana and Sui. While other projects like Sei and Monad have garnered significant attention and funding, Reddio has maintained a low profile, focusing on showcasing its test network's stability with high transactions per second (TPS) data. Recently, Reddio announced its intention to validate the ecological value of parallel EVM within the Ethereum ecosystem. The parallel EVM addresses the limitations of the original EVM's single-threaded execution and sequential transaction processing. By leveraging modern hardware capabilities, such as CPUs and GPUs, along with asynchronous I/O storage and state access optimizations, it enables simultaneous execution of large-scale transactions. Reddio's white paper outlines a GPU-based execution network that uses a CUDA "code translator" to convert standard EVM opcodes into complex computational tasks executable on GPUs. This setup, combined with I/O optimizations and optimistic concurrency control, enhances transaction processing capabilities. The parallel EVM's hardware performance advantage aligns naturally with AI applications, which require large-scale parallel computation and intensive processing. This synergy opens up new possibilities for deploying AI models on the blockchain, allowing smart contracts to directly manage AI tasks. Additionally, the integration of technologies like zero-knowledge proofs (ZK) and trusted execution environments (TEE) can enhance data privacy and verifiability, facilitating the native fusion of blockchain and AI. Potential applications include real-time AI inference, AI oracles, and off-chain AI strategy optimization.