The development of in-memory computing has opened up possibilities to build next-generation non-von-Neumann computing architecture. Implementation of logic functions within the memristors can significantly improve the energy efficiency and alleviate the bandwidth congestion issue. In this work, the demonstration of arithmetic logic unit functions is presented in a memristive crossbar with implemented non-volatile Boolean logic and arithmetic computing. For logic implementation, a standard operating voltage mode is proposed for executing reconfigurable stateful IMP, destructive OR, NOR, and non-destructive OR logic on both the word and bit lines. No additional voltages are needed beyond "V P " and its negative component. With these basic logic functions, other Boolean functions are constructed within five devices in at most five steps. For arithmetic computing, the fundamental functions including an n-bit full adder with high parallelism as well as efficient increment, decrement, and shift operations are demonstrated. Other arithmetic blocks, such as subtraction, multiplication, and division are further designed. This work provides solid evidence that memristors can be used as the building block for in-memory computing, targeting various low-power edge computing applications.

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Memristive stateful logic has emerged as a promising next-generation in-memory computing paradigm to address escalating computing-performance pressures in traditional von Neumann architecture. Here, we present a nonvolatile reprogrammable logic method that can process data between different rows and columns in a memristive crossbar array based on material implication (IMP) logic. Arbitrary Boolean logic can be executed with a reprogrammable cell containing four memristors in a crossbar array. In the fabricated Ti/HfO2/W memristive array, some fundamental functions, such as universal NAND logic and data transfer, were experimentally implemented. Moreover, using eight memristors in a 2  ×  4 array, a one-bit full adder was theoretically designed and verified by simulation to exhibit the feasibility of our method to accomplish complex computing tasks. In addition, some critical logic-related performances were further discussed, such as the flexibility of data processing, cascading problem and bit error rate. Such a method could be a step forward in developing IMP-based memristive nonvolatile logic for large-scale in-memory computing architecture.

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