An analysis of Reddio's latest White Paper: Automated AI + Parallel EVM, filling the short board of the ETH community

After reading the latest White Paper released by Reddio, it indeed integrates automated AI execution into the epic of EVM, effectively filling the gap in the AI track of the entire ETH community. It makes a lot of sense. So, why can parallel EVM seamlessly connect with AI? What are the underlying logic and technical principles? Let me briefly explain my understanding:

1. The narrative of 'Parallel EVM' has always been characterized as a key player in bridging the gap between the old and slow EVM ecosystem and high-performance chain technologies such as Solana and Sui. Therefore, the previous market hype around Sei's expectations and Monad's massive $225 million financing have propelled Parallel EVM to unprecedented heights.

In contrast, Reddio, as a parallel EVM public chain led by Paradigm, seems to be much lower-key, without the hype of financing, ICOs, KOL rounds, and other market expectations, just always showing off its test network's stable TPS data. Recently, the official announcement of the snapshot clearly aims to take the lead, verifying the ecological value of the parallel EVM in the ETH Kang ecology.

2. So, why is parallel EVM an effective supplement to the technical capabilities of the ETH ecosystem? Simply put, the original single-threaded execution and serial transaction order execution of EVM are inherent limitations. Parallel EVM utilizes the parallel computing capabilities of modern hardware (CPU, GPU), coupled with some I/O asynchronous storage processing, state access optimization, etc., to achieve simultaneous execution of large-scale batch transactions.

The technical implementation logic revealed in ReddioWhite Paper is roughly as follows. Reddio has an execution network composed of GPU nodes and uses a CUDA 'encoding translator' to translate general EVM opcodes into complex, intensive computing tasks that can be implemented inside the GPU. In addition, other optimizations such as I/O asynchronous storage, state access management, optimistic concurrency control, etc., have achieved the ability to process transactions in parallel.

3. Since parallel EVM essentially leverages the performance advantages of 'hardware,' AI applications naturally require large-scale parallel computing and intensive computational processing. A powerful hardware can simultaneously parallelize EVM and AI application scenarios to maximize utility. In this way, another layer of narrative imagination space for parallel EVM + AI is opened. Parallel EVM chains can achieve on-chain deployment of large AI models, allowing smart contracts to directly control and schedule AI, while also applying related data privacy and verifiability capabilities such as ZK, TEE, etc., to achieve native integration of blockchain and AI. For example, real-time AI inference, AI Oracle, off-chain AI transaction strategy optimization, and so on.