Robust Compute-in Memory using Memristors



Prof. Mehdi B. Tahoori

Nr.: TA 782/36-1

Chair of Dependable Nano-Computing, Karlsruher Inst. f. Technologie (KIT)


Dr. Dirk Wouters

Nr.: WO 2090/1-1

Inst. f. Werkstoffe der Elektrotechnik 2, RWTH Aachen


Special Session – Non-Volatile Memories: Challenges and Opportunities for Embedded System Architectures with Focus on Machine Learning Applications

Jörg Henkel, Lokesh Siddhu, Lars Bauer, Jürgen Teich, Stefan Wildermann, Mehdi Tahoori, Mahta Mayahinia, Jeronimo Castrillon, Asif Ali Khan, Hamid Farzaneh, João Paulo C. de Lima, Jian-Jia Chen, Christian Hakert, Kuan-Hsun Chen, Chia-Lin Yang, Hsiang-Yun Cheng

Proceedings of the 2023 International Conference on Compilers, Architecture, and Synthesis of Embedded Systems (CASES), Sep 2023

DOI: tba

Timing-accurate simulation framework for NVM-based compute-in-memory architecture exploration

Vincent Rietz; Christopher Münch, Mahta Mayahinia, Mehdi Tahoori

it - Information Technology, Vol. 65, 1-2, pages 13–29, 03.05.2023

DOI: 10.1515/itit-2023-0019

MemrisTec Young Researcher Award
A failure analysis framework of ReRAM In-Memory Logic operations
A. Jafari; C. Bengel; M. Mayahinia; R. Waser; D. Wouters; S. Menzel; M. Tahoori 2022 IEEE International Test Conference in Asia (ITC-Asia), 24-26 August 2022, Taipei, Taiwan DOI: 10.1109/ITCAsia55616.2022.00022
Reliability of Computing-In-Memory Concepts Based on Memristive Arrays

D. J. Wouters; L. Brackmann; A. Jafari; C. Bengel; M. Mayahinia; R. Waser; S. Menzel; M. Tahoori

2022 International Electron Devices Meeting (IEDM), 03-07 December 2022, San Francisco, CA, USA

DOI: 10.1109/IEDM45625.2022.10019423

Project Description

Emerging applications (such as Internet-of-Things and Big Data analytics) are posing serious challenges on current computer architectures and technologies. Therefore, there is an urgent need to explore alternative architectures, not only to further increase the computing efficiency at lower cost, but also to further reduce the overall energy. Further, new device technologies are emerging that, in combination with new architectures, may compete with no-longer-scaling CMOS.

Moving computation to memory (data-centric computing) is an emerging computing paradigm shown to have a huge potential in terms of overall computing efficiency. This computing paradigm is also referred to as Computation-in-Memory (CIM). Moving the computation to the memory (rather than doing it in the CPU) will significantly reduce the communication and therefore reduce the power consumption and increase the performance.

The most promising solutions for CIM architectures are based on the use of emerging device technologies, such as resistive or magneto-resistive devices, that are able to act as both storage and information processing unit. These, more generally called memristive devices, reduce the overall energy consumption as the devices are non-volatile and the leakages is practically zero, and also they do not require any refresh as it is the case for DRAM, etc. Hence, memristive devices favour increasing system complexity and performance at lower power consumption; thus, providing the scientific community with opportunities for new computer architecture innovations being able to track today’s limitation.

However, realizing such a paradigm strongly depends on the development of high quality, reliable and power efficient circuit primitives that enable both storage and computing.This project aims at designing and demonstrating high quality, reliable, robust and energy efficient primitive circuits based on memristive devices to enable computation-in-memory architectures. In particular, we aim at the development of robust CIM circuits based on memristive device by i) carefully modeling and analyzing the effects of memristive device (un)reliability on CIM operation and ii) develop reliability-aware and fault/variation tolerant design of CIM circuits.

Further involved scientists


Atousa Jafari

Chair of Dependable Nano-Computing, Karlsruher Inst. f. Technologie (KIT)


Leon Brackmann

Inst. f. Werkstoffe der Elektrotechnik 2, RWTH Aachen


Mahta Mayahinia

Chair of Dependable Nano-Computing, Karlsruher Inst. f. Technologie (KIT)

Shanmukha Mangadahalli Siddaramu_transparent

Shanmukha Mangadahalli Siddaramu

Chair of Dependable Nano-Computing, Karlsruher Inst. f. Technologie (KIT)