电阻随机存取存储器
神经形态工程学
可扩展性
材料科学
非易失性存储器
计算机科学
功率消耗
纳米技术
电气工程
功率(物理)
光电子学
人工智能
工程类
人工神经网络
电压
物理
操作系统
量子力学
出处
期刊:Cornell University - arXiv
日期:2023-04-29
标识
DOI:10.48550/arxiv.2305.00166
摘要
Resistive random-access memory (RRAM) is a promising candidate for next-generation memory devices due to its high speed, low power consumption, and excellent scalability. Metal oxides are commonly used as the oxide layer in RRAM devices due to their high dielectric constant and stability. However, to further improve the performance of RRAM devices, recent research has focused on integrating artificial intelligence (AI). AI can be used to optimize the performance of RRAM devices, while RRAM can also power AI as a hardware accelerator and in neuromorphic computing. This review paper provides an overview of the combination of metal oxides-based RRAM and AI, highlighting recent advances in these two directions. We discuss the use of AI to improve the performance of RRAM devices and the use of RRAM to power AI. Additionally, we address key challenges in the field and provide insights into future research directions
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