大数据时代基于忆阻器的CiM架构

E. Apollos, Steve A. Adeshina, N. A. Nnanna
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引用次数: 3

摘要

本文介绍了基于记忆电阻器的内存计算(CiM)架构,以缓解当今由于大数据时代的出现而面临的传统CMOS技术和von Neumann架构的挑战。本文通过设计和仿真表明,忆阻器材料需要具备高开关速度、非易失性、使用交叉棒阵列的紧凑密度、混沌和非二进制能力、几乎为零功率和电流泄漏,从而使基于忆阻器的内存计算架构成为缓解传统计算机架构和CMOS工艺技术造成的这些大数据计算限制所需的技术革命。随着晶体管尺寸降至45纳米以下,CMOS技术和冯·诺伊曼架构已经达到了制造物理极限,从而导致晶体管中信号传播的金属互连延迟增加、功率泄漏、数据可靠性低、安全问题,以及随着尺寸降至45纳米以下,开发和建造CMOS芯片制造设施的成本高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Memristor-Based CiM Architecture for Big Data Era
In this paper, memristor based Computation-in-Memory (CiM) architecture is introduced to mitigate today’s challenges faced with the conventional CMOS technologies and von Neumann architecture due to the emergence of Big data era. Memristor material has shown through design and simulation as presented in this paper where necessary to have high switching speed, non-volatile capability, compact density using crossbar array, chaotic and non-binary ability, almost zero power and current leakage thus making memristor-based Computation-in-Memory architecture the needed technology revolution to mitigate these Big data computing limits caused by the conventional computer architecture and CMOS process technologies. The CMOS technologies and von Neumann architecture have reached fabrication physical limit as transistor scaling goes below 45nm technology node thus resulting to increasing delays that occur in the metal interconnect for signal propagation in transistors, power leakages, low data reliability, security issues, and high cost of developing and building CMOS chip-fabrication facilities as scaling goes down below 45nm.
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