Phase Change Random Access Memory for Neuro-Inspired Computing

Neuro-inspired computing using emerging memristors plays an increasingly significant role for the realization of artificial intelligence and thus has attracted widespread interest in the era of big data. Thanks to the maturity of technology and the superiority of device performance, phase change random access memory (PCRAM) is a promising candidate for both nonvolatile memories and neuro-inspired computing. Recently many efforts have been carried out to achieve the biological behavior using PCRAM and to clarify the related working mechanism. In order to further improve device performances, it is helpful and urgent to summarize and discuss the PCRAM solution for neuro-inspired computing. In this paper, fundamentals, principles, recent progresses, existing challenges, and mainstream solutions are reviewed, and a brief outlook is highlighted and introduced, with the expectation to expound future directions.

Automated summary

Utilizing emerging memristors for neuro-inspired computing has become increasingly important for artificial intelligence in the era of big data. PCRAM, with its maturity in technology and superior device performance, is a promising candidate for both nonvolatile memories and neuro-inspired computing. Efforts have been made to achieve biological behavior using PCRAM and to clarify its working mechanism, with the goal of further improving device performances.