基于互补电阻开关的内存计算关联电容网络中电容潜行路径的消除

Tobias Ziegler, Leon Brackmann, T. Hennen, C. Bengel, S. Menzel, D. Wouters
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引用次数: 0

摘要

将计算从中央处理单元转移到存储器是一种很有前途的方法,可以降低对冯-诺伊曼瓶颈的压力,并减少数据传输的总能耗。一个很有前途的内存计算概念是由Kavehei等人介绍的联想电容网络。数字信息存储在互补电阻开关中,该开关可以使用非破坏性读出方案读取。基于JART VCM模型的仿真结果验证了原始输入编码的工作原理,并识别了电容性潜径电流的存在。本文提出了一种新的输入编码方法,既可以防止电容性潜径,又可以提高汉明距离(HD)之间的电压差。
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Eliminating Capacitive Sneak Paths in Associative Capacitive Networks based on Complementary Resistive Switches for In-Memory Computing
Shifting computations from the central processing unit to the memory is a promising approach to lower the stress on the Von-Neumann bottleneck and to reduce the total energy consumption spend on data transfer. One promising concept for in-memory computations is the associative capacitive network introduced by Kavehei et al.. The digital information is stored in complementary resistive switches which can be read using a non-destructive read out scheme. Simulation results based on the JART VCM vlb model demonstrate the working principle of the original input encoding and the existence of capacitive sneak path currents is identified. A new input encoding is proposed in this work which not only prevents capacitive sneak paths but also improves the voltage difference between Hamming Distances (HD).
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