Compute-in-Memory Based on Emerging Non-Volatile Memories: RRAM, MRAM, and FeRAM
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Compute-in-Memory Based on Emerging Non-Volatile Memories: RRAM, MRAM, and FeRAM

Qianye Han 1*
1 TONGJI University
*Corresponding author: 2454210@tongji.edu.cn
Published on 26 November 2025
Volume Cover
ACE Vol.209
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-80590-561-5
ISBN (Online): 978-1-80590-562-2
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Abstract

In the era of artificial intelligence, Internet of things and big data, processing massive data puts forward unprecedented requirements for the throughput and energy efficiency of computing systems. In traditional von Neumann architectures, frequent data movement between processor and memory results in significant energy consumption and latency, known as the“Memory Wall” problem. In this paper, the principle, application and performance of variable resistance random-access memory (RRAM), magnetoresistive random-access memory (MRAM) and ferroelectric random-access memory (Feram) in the memory computing (CIM) structure are studied in depth. CIM technology is considered to be the key to overcome the“Memory wall” bottleneck inherent in the traditional von Neumann architecture. Firstly, the physical mechanism and characteristics of these three storage technologies are systematically described. Subsequently, a detailed analysis of their effectiveness in various application scenarios is provided through innovative simulation designs: RRAM-based neuromorphic chips (neurrams) exhibit superior energy efficiency in simulation calculations; MRAM exhibits performance close to conventional memory in non-volatile caching and in-memory logic applications; while FeRAM has unique advantages in ultra-low-power binary neural networks (BNNS). A comprehensive comparative analysis demonstrates the complementarity of their technical paths, and proposes future integration strategies for heterogeneous computing systems. This study adopts a co-design perspective across device, circuit, and architecture levels to provide important theoretical foundations and design insights for the development of next-generation efficient heterogeneous computing systems.

Keywords:

CIM, non-volatile memory, RRAM, neuromorphic computing

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Han,Q. (2025). Compute-in-Memory Based on Emerging Non-Volatile Memories: RRAM, MRAM, and FeRAM. Applied and Computational Engineering,209,7-14.

References

[1]. Hu, M., Strachan, J. P., Li, Z., Grafals, E. M., Davila, N., Graves, C., Lam, S., Ge, N., Yang, J. J. and Williams, R. S. (2016) Dot-product engine for neuromorphic computing: Programming 1T1M crossbar to accelerate matrix-vector multiplication. Proceedings of the 53rd ACM/EDAC/IEEE Design Automation Conference (DAC), 1–6.

[2]. Kim, J., Chen, J. and Wang, J.-P. (2019) Spin-transfer torque magnetic memory as a commodity memory device. IEEE Transactions on Magnetics, 55(5), 1–8.Manipatruni, S., Nikonov, D. E. and Young, I. A. (2018) Beyond CMOS computing with spin and polarization. Nature Physics, 14(4), 338–343.

[3]. Mikolajick, T., Slesazeck, S., Park, M. H. and Schroeder, U. (2018) Ferroelectric hafnium oxide for ferroelectric random-access memories and ferroelectric field-effect transistors. MRS Bulletin, 43(5), 340–346.

[4]. Takahashi, S. and Sakai, S. (2018) Embedded ferroelectric memory technology for energy-efficient computing. 2018 IEEE International Electron Devices Meeting (IEDM), 18.4.1–18.4.4.

[5]. Zhang, T., Li, Y. and Chen, C. L. P. (2018) Edge computing and its role in smart systems. IEEE Access, 6, 72622–72634.

[6]. Wong, H.-S. P., Lee, H.-Y., Yu, S., Chen, Y.-S., Wu, Y., Chen, P.-S., Lee, B., Chen, F. T. and Tsai, M.-J. (2012) Metal–oxide RRAM. Proceedings of the IEEE, 100(6), 1951–1970.

[7]. Apalkov, D., Dieny, B. and Slaughter, J. M. (2016) Magnetoresistive random access memory. Proceedings of the IEEE, 104(10), 1796–1830.

[8]. Jin, D.-Y., Chen, H., Wang, Y., Zhang, W.-R., Na, W.-C., Guo, B., Wu, L., Yang, S.-M. and Sun, S. (2020) Process deviation based electrical model of voltage controlled magnetic anisotropy magnetic tunnel junction and its application in read/write circuits. Acta Physica Sinica, 69(19), 198502.

[9]. Mikolajick, T., Slesazeck, S., Mulaosmanovic, H., Park, M. H., Fichtner, S., Lomenzo, P. D. and Hoffmann, M. (2020) Next generation ferroelectric memories: Fundamentals and future perspectives. Applied Physics Letters, 117(9), 090501.

[10]. Chiu, Y.-C., Lee, J.-Y., Chang, M.-F., Wu, J.-Y., Shen, W.-C., Lee, R.-S., King, Y.-C., Lin, C.-J. and Chen, P.-H. (2022) A 65nm 4Kb algorithm-dependent computing-in-memory SRAM unit macro with 2.3ns and 55.8TOPS/W all-precision-pipeline-capable binary-ternary-bitwise AI inference. *2022 IEEE International Solid-State Circuits Conference (ISSCC)*, 65, 1–3.

[11]. Manipatruni, S., Nikonov, D. E. and Young, I. A. (2018) Beyond CMOS computing with spin and polarization. Nature Physics, 14(4), 338–343.

[12]. Manipatruni, S., Nikonov, D. E., Lin, C.-C., Gosavi, T. A., Liu, H., Prasad, B., Young, I. A. and Naeemi, A. (2020) STT-MRAM based last-level cache for high-performance computing: System-level analysis and optimization. IEEE Transactions on Magnetics, 56(2), 1–7.

[13]. Onaya, T., Nabatame, T. and Toriumi, A. (2021) FeRAM-based in-memory computing for binary neural networks with energy-efficient polarization switching. 2021 IEEE International Electron Devices Meeting (IEDM), 3.4.1–3.4.4.

[14]. Khan, M. W., Jaiswal, A. and Alam, M. A. (2022) STT-MRAM for non-volatile cache memories: Perspectives and challenges. IEEE Transactions on Electron Devices, 69(6), 2857–2865.

[15]. Kumar, A. and Kim, Y. (2022) Emerging memory technologies for energy-efficient edge intelligence: A review. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 30(5), 567–580.

[16]. Ielmini, D. and Wong, H.-S. P. (2023) Machine learning using resistive random access memory: A review from a co-design perspective. IEEE Transactions on Electron Devices, 70(9), 4464–4475.

[17]. Klein, J.-O., Khan, M. W., et al. (2022) A review of STT-MRAM: From the device to the system. ACM Journal on Emerging Technologies in Computing Systems, 18(2), Article 30.

[18]. Grand View Research. (2023). *AI accelerator market size, share & trends analysis report 2023-2030* (Report ID: GVR-2-68038-841-3). https: //www.grandviewresearch.com/industry-analysis/ai-accelerator-market-report

References

[1]. Hu, M., Strachan, J. P., Li, Z., Grafals, E. M., Davila, N., Graves, C., Lam, S., Ge, N., Yang, J. J. and Williams, R. S. (2016) Dot-product engine for neuromorphic computing: Programming 1T1M crossbar to accelerate matrix-vector multiplication. Proceedings of the 53rd ACM/EDAC/IEEE Design Automation Conference (DAC), 1–6.

[2]. Kim, J., Chen, J. and Wang, J.-P. (2019) Spin-transfer torque magnetic memory as a commodity memory device. IEEE Transactions on Magnetics, 55(5), 1–8.Manipatruni, S., Nikonov, D. E. and Young, I. A. (2018) Beyond CMOS computing with spin and polarization. Nature Physics, 14(4), 338–343.

[3]. Mikolajick, T., Slesazeck, S., Park, M. H. and Schroeder, U. (2018) Ferroelectric hafnium oxide for ferroelectric random-access memories and ferroelectric field-effect transistors. MRS Bulletin, 43(5), 340–346.

[4]. Takahashi, S. and Sakai, S. (2018) Embedded ferroelectric memory technology for energy-efficient computing. 2018 IEEE International Electron Devices Meeting (IEDM), 18.4.1–18.4.4.

[5]. Zhang, T., Li, Y. and Chen, C. L. P. (2018) Edge computing and its role in smart systems. IEEE Access, 6, 72622–72634.

[6]. Wong, H.-S. P., Lee, H.-Y., Yu, S., Chen, Y.-S., Wu, Y., Chen, P.-S., Lee, B., Chen, F. T. and Tsai, M.-J. (2012) Metal–oxide RRAM. Proceedings of the IEEE, 100(6), 1951–1970.

[7]. Apalkov, D., Dieny, B. and Slaughter, J. M. (2016) Magnetoresistive random access memory. Proceedings of the IEEE, 104(10), 1796–1830.

[8]. Jin, D.-Y., Chen, H., Wang, Y., Zhang, W.-R., Na, W.-C., Guo, B., Wu, L., Yang, S.-M. and Sun, S. (2020) Process deviation based electrical model of voltage controlled magnetic anisotropy magnetic tunnel junction and its application in read/write circuits. Acta Physica Sinica, 69(19), 198502.

[9]. Mikolajick, T., Slesazeck, S., Mulaosmanovic, H., Park, M. H., Fichtner, S., Lomenzo, P. D. and Hoffmann, M. (2020) Next generation ferroelectric memories: Fundamentals and future perspectives. Applied Physics Letters, 117(9), 090501.

[10]. Chiu, Y.-C., Lee, J.-Y., Chang, M.-F., Wu, J.-Y., Shen, W.-C., Lee, R.-S., King, Y.-C., Lin, C.-J. and Chen, P.-H. (2022) A 65nm 4Kb algorithm-dependent computing-in-memory SRAM unit macro with 2.3ns and 55.8TOPS/W all-precision-pipeline-capable binary-ternary-bitwise AI inference. *2022 IEEE International Solid-State Circuits Conference (ISSCC)*, 65, 1–3.

[11]. Manipatruni, S., Nikonov, D. E. and Young, I. A. (2018) Beyond CMOS computing with spin and polarization. Nature Physics, 14(4), 338–343.

[12]. Manipatruni, S., Nikonov, D. E., Lin, C.-C., Gosavi, T. A., Liu, H., Prasad, B., Young, I. A. and Naeemi, A. (2020) STT-MRAM based last-level cache for high-performance computing: System-level analysis and optimization. IEEE Transactions on Magnetics, 56(2), 1–7.

[13]. Onaya, T., Nabatame, T. and Toriumi, A. (2021) FeRAM-based in-memory computing for binary neural networks with energy-efficient polarization switching. 2021 IEEE International Electron Devices Meeting (IEDM), 3.4.1–3.4.4.

[14]. Khan, M. W., Jaiswal, A. and Alam, M. A. (2022) STT-MRAM for non-volatile cache memories: Perspectives and challenges. IEEE Transactions on Electron Devices, 69(6), 2857–2865.

[15]. Kumar, A. and Kim, Y. (2022) Emerging memory technologies for energy-efficient edge intelligence: A review. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 30(5), 567–580.

[16]. Ielmini, D. and Wong, H.-S. P. (2023) Machine learning using resistive random access memory: A review from a co-design perspective. IEEE Transactions on Electron Devices, 70(9), 4464–4475.

[17]. Klein, J.-O., Khan, M. W., et al. (2022) A review of STT-MRAM: From the device to the system. ACM Journal on Emerging Technologies in Computing Systems, 18(2), Article 30.

[18]. Grand View Research. (2023). *AI accelerator market size, share & trends analysis report 2023-2030* (Report ID: GVR-2-68038-841-3). https: //www.grandviewresearch.com/industry-analysis/ai-accelerator-market-report

Cite this article

Han,Q. (2025). Compute-in-Memory Based on Emerging Non-Volatile Memories: RRAM, MRAM, and FeRAM. Applied and Computational Engineering,209,7-14.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-MCEE 2026 Symposium: Advances in Sustainable Aviation and Aerospace Vehicle Automation

ISBN: 978-1-80590-561-5(Print) / 978-1-80590-562-2(Online)
Editor: Ömer Burak İSTANBULLU
Conference website: https://2025.confmcee.org/kayseri.html
Conference date: 14 November 2025
Series: Applied and Computational Engineering
Volume number: Vol.209
ISSN: 2755-2721(Print) / 2755-273X(Online)

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