A Memristor-Based In-Memory Adaptive Approximate Adder

This work presents the design of a memristor-based in-memory adaptive approximate adder aimed at achieving high energy efficiency and reduced computation latency for modern computing applications. With the limitations of conventional CMOS architectures, particularly the von Neumann bottleneck caused by separate memory and processing units, in-memory computing using memristors has emerged as a promising solution. Memristors provide non-volatile storage along with logic computation capability, enabling data processing directly within memory arrays.In the proposed design, an adaptive approximate adder is implemented using memristive devices, where approximation techniques are applied to reduce circuit complexity and power consumption while maintaining acceptable computational accuracy. The level of approximation is dynamically controlled based on input conditions, allowing a trade-off between accuracy and efficiency. The architecture minimizes data movement, reduces delay, and improves overall system throughput. Simulation results indicate that the proposed design achieves significant improvements in power consumption, speed, and area efficiency compared to conventional exact adders. This makes it highly suitable for applications in error-tolerant systems such as multimedia processing, machine learning, and IoT devices.

 

Keywords

 

Memristor, In-Memory Computing, Approximate Adder, Low Power, Adaptive Design, Non-Volatile Memory, Nanoelectronics