What kind of new computation technology does ChatGPT need?

Recently, ChatGPT4 has become the new hot spot with Microsoft office and google products, with the AI tools generation, which kind of new computation technology support that? Computing in Memory (CIM) technology is an emerging computing architecture that integrates storage and computing functions into the same chip. This technology has a wide range of applications in big data processing, artificial intelligence, and machine learning. For large natural language processing models like ChatGPT4, CIM technology is one of the key factors for performance improvement. It helps companies reduce energy consumption, increase computing efficiency, and achieve higher performance. Firstly, CIM technology can help reduce data transfer time and energy consumption. In traditional computer architectures, data needs to be transferred from storage units to computing units, which consumes a lot of time and energy. In CIM technology, storage and computing units are directly integrated into the same chip, and data can be directly computed in the storage unit, avoiding data transfer time and energy consumption. Secondly, CIM technology can achieve more efficient parallel computing. In large-scale data processing and machine learning tasks, a lot of parallel computing is required. In traditional computer architectures, multiple computing units are needed to achieve parallel computing, which increases complexity and energy consumption. In CIM technology, each storage unit can perform computing, achieving more refined parallel computing. Furthermore, CIM technology can achieve a more tightly coupled training and inference combination. In machine learning tasks, both training and inference processes are required. In traditional computer architectures, data needs to be transferred from storage units to computing units to achieve training and inference processes. In CIM technology, storage and computing units are integrated into the same chip, achieving a more tightly coupled training and inference combination, thus improving computing efficiency and performance.