Uncorrelated stochastic bitstream generation and arithmetic computations using Cu:ZnO memristors

One popular approach under approximate computing is stochastic computing, wherein values are encoded in bitstreams to perform arithmetic operations in a low-power and computationally inexpensive manner. The stochastic computing paradigm, despite many advantages, is hindered by the need to generate uncorrelated input bitstream, which results in additional hardware overhead. In this work, Pt/Cu:ZnO/Nb:STO memristive devices were experimentally found to exhibit random switching behavior. The switching time was modeled as a Poisson distribution and the obtained results were utilized to develop a Stochastic Voltage Threshold Adaptive Memristor (SVTAM) model. Based on this circuit model, an innovative method to perform addition, multiplication and sum of product operations has been proposed by employing the memristor crossbar architecture. This implementation is advantageous as the row inputs can be reused across different columns of the crossbar to implement a large number of functions simultaneously in a compact arrangement. The functionality was verified using just 50 input pulses, and the error is just 1% when the number of pulses was increased to 200. In addition, the output is an uncorrelated bitstream, making it compatible with the existing stochastic computing circuits. This work presents a novel solution towards advancement in the field of approximate computing.

Automated summary

In this research, Pt/Cu:ZnO/Nb:STO memristive devices were found to exhibit random switching behavior, leading to the development of a Stochastic Voltage Threshold Adaptive Memristor (SVTAM) model. This model enables an innovative method to perform arithmetic operations using a memristor crossbar architecture, allowing for compact and simultaneous implementation of a large number of functions. The functionality was successfully verified with low input pulses and minimal error, making it a novel solution in the field of approximate computing.