数据存储和大脑启发计算的新兴记忆技术:印度研究见解的全球视角,重点是电阻性记忆

IF 0.8 4区 综合性期刊 Q3 MULTIDISCIPLINARY SCIENCES Proceedings of the National Academy of Sciences, India Section A: Physical Sciences Pub Date : 2023-06-04 DOI:10.1007/s40010-023-00828-w
Sandip Lashkare, Wasi Uddin, Kumar Priyadarshi, Udayan Ganguly
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引用次数: 0

摘要

本文概述了新兴的记忆材料及其在先进的大脑启发计算技术中的作用。它从过去50年来存储技术的进步以及NAND闪存在存储市场的出现和主导地位开始。闪存虽然以大批量生产和低成本等优势占据了存储器市场的主导地位,但与动态随机存储器相比,其延迟时间存在差距。为了解决这个问题,世界各地都在探索各种可能取代闪存的非易失性存储器。在这里,概述了各种主要的新兴非易失性存储器(NVM)技术。随着NVMs作为一种存储解决方案的全球观点,简要讨论了NVMs在印度的研究,重点是电阻随机存取存储器和相变存储器。此外,我们还讨论了对大脑启发的先进计算技术的需求,如神经形态计算、内存计算,以及nvm在这些大脑启发的计算技术中的应用。最后,介绍了各种nvm的独特特性,以模拟神经形态计算和内存计算解决方案所需的突触、神经元功能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Emerging Memory Technologies for Data Storage and Brain-Inspired Computation: A Global View with Indian Research Insights with a Focus on Resistive Memories

This article is an overview of emerging memory materials and their role in advanced brain-inspired computing technologies. It starts with the progress of memory technologies over the last 50 years along with emergence and dominance of NAND flash in the memory market. Though flash is currently leading the memory market due to its high volume manufacturing and low cost, it has a latency gap with dynamic random access memory. To address this, various nonvolatile memories have been explored across the world potentially to replace flash. Here, an overview of various major emerging nonvolatile memory (NVM) technologies is presented. Along with the global view of NVMs as their current status as a storage solution, the research of NVMs in India is discussed briefly with a focus on resistance random access memory and phase change memory. Further, the need of brain-inspired advanced computing technologies like neuromorphic computing, in-memory computing are discussed along with the utility of the NVMs for such brain-inspired computing technologies. Finally, various NVMs are presented for their unique characteristic to mimic synapse, neuron functionalities as required for neuromorphic computing and for in-memory computing solutions.

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来源期刊
CiteScore
2.60
自引率
0.00%
发文量
37
审稿时长
>12 weeks
期刊介绍: To promote research in all the branches of Science & Technology; and disseminate the knowledge and advancements in Science & Technology
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