The 6G systems are established on Artificial Intelligence (AI) and distributed ledger such as blockchain. The training of AI requires lots of computing resources, which would raise the cost of 6G. In blockchains, each miner has plenty of computing resources, which could be used for AI training.
As current blockchain technologies are criticized for wasting computing resources, a recent paper proposes a consensus for connecting the computing resource consumed by AI training and block mining. This way, the computing efficiency in 6G systems is improved. The matrix multiplication calculation (MMC) is used to achieve it. The miners conduct the target hash value search based on both the traditional block header and the result of MMC. Experiments confirmed that the suggested system salvages up to 80 percent computing power from pure block mining for parallel AI training.
The sixth generation (6G) systems are generally recognized to be established on ubiquitous Artificial Intelligence (AI) and distributed ledger such as blockchain. However, the AI training demands tremendous computing resource, which is limited in most 6G devices. Meanwhile, miners in Proof-of-Work (PoW) based blockchains devote massive computing power to block mining, and are widely criticized for the waste of computation. To address this dilemma, we propose an Evolved-Proof-of-Work (E-PoW) consensus that can integrate the matrix computations, which are widely existed in AI training, into the process of brute-force searches in the block mining. Consequently, E-PoW can connect AI learning and block mining via the multiply used common computing resource. Experimental results show that E-PoW can salvage by up to 80 percent computing power from pure block mining for parallel AI training in 6G systems.
Research paper: Wei, Y., An, Z., Leng, S., and Yang, K., “Connecting AI Learning and Blockchain Mining in 6G Systems”, 2021. Link: https://arxiv.org/abs/2104.14088