In‐memory computing enabled by advanced nonvolatile memory technologies, such as memristors and memristive devices, emerges as a promising approach to accelerate certain data‐intensive algorithms, and thus outperforms the von Neumann computing in terms of processing latency and energy efficiency. In this work, an efficient method to calculate the Hamming weight (HW) of a binary string in a one‐transistor‐one‐resistor (1T1R) memristive array is proposed, which can be beneficial for various computation tasks. Specifically, the target string is converted to a voltage vector and multiplies with an “all‐1” string pre‐stored in the resistance of the row, which equals to a binary matrix multiplication or AND logic operation. The in situ stored HW calculation result is then read out through a current accumulation operation. As a proof‐of‐concept demonstration, 4 bit and 8 bit HW calculation is successfully implemented in experiment and simulation, respectively. In addition, the influence of the resistance variation on the calculation correctness is discussed. This work broadens the application range of using emerging nonvolatile memories for classical information processing in hardware level with high efficiency.

中文翻译：

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1T1R忆阻阵列中的存储器内Hamming权重计算

由先进的非易失性存储器技术（例如忆阻器和忆阻器件）支持的内存计算是加速某些数据密集型算法的有前途的方法，因此在处理延迟和能效方面优于冯·诺依曼计算。在这项工作中，提出了一种有效的方法来计算单晶体管单电阻（1T1R）忆阻阵列中的二进制字符串的汉明权重（HW），这对于各种计算任务都是有益的。具体来说，目标串将转换为电压矢量，并与行电阻中预存储的“ all-1”串相乘，这等于二进制矩阵乘法或AND逻辑运算。然后通过电流累加操作读出就地存储的硬件计算结果。作为概念验证的演示，分别在实验和仿真中成功实现了4位和8位硬件计算。另外，讨论了电阻变化对计算正确性的影响。这项工作拓宽了将新兴的非易失性存储器用于硬件级经典信息处理的高效应用范围。