IMAGIN: Library of IMPLY and MAGIC NOR Based Approximate Adders for In-Memory Computing

Chandan Kumar Jha, Phrangboklang Lyngton Thangkhiew, Kamalika Datta, Rolf Drechsler

In: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits IEEE 2022.


In-memory computing (IMC) has attracted significant interest in recent years as it aims to bridge the memory bottleneck in Von Neumann architectures. IMC also improves the energy efficiency in these architectures. Another technique that has been explored to reduce energy consumption is the use of approximate circuits, targeted toward error resilient applications. These applications have addition as one of their most frequently used operations. In literature, CMOS based approximate adder libraries have been implemented to help designers choose from a variety of designs depending upon the output quality requirements. However, the same is not true for memristor based approximate adders targeted for IMC architectures. Hence, in this work, we developed a framework to generate approximate adder designs with varying output errors for 8, 12, and 16-bit adders. We implemented a state-of-the-art scheduling algorithm to obtain the best mapping of these approximate adder designs for IMC. We performed an exhaustive design space exploration to obtain the pareto-optimal approximate adder designs for various design and error metrics. We then proposed IMAGIN, a library of approximate adders compatible with memristor based IMC architecture, which are based on IMPLY and MAGIC design styles. We also performed mean filtering on the Kodak image dataset using the approximate adders from the IMAGIN library. IMAGIN can help designers select from a wide variety of approximate adders depending upon the output quality requirements and serve as benchmarks for future research in this direction. All pareto-optimal designs will be made available at

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence