2023, Volume 7
2022, Volume 6
2021, Volume 5
2020, Volume 4
2019, Volume 3
2018, Volume 2
2017, Volume 1
1Department of Computer Science, School of Computing and Information Sciences, C.K Tedam University of Technology and Applied Sciences, Navrongo, Ghana
2Department of Computer Science/Applied Sciences and Technology, Ho Technical University, Ho, Ghana
3Gracecoms Institute of Technology, Assin Foso, Ghana
4Local Government Service, Accra, Ghana
Central processing units (CPUs) in modern computing devices rely on computer memory systems to store and retrieve the data they require to perform their duties. This research covers the types, functions, and historical evolution of computer memory systems. It also looks at new developments in memory technology that are influencing the direction of computing. Using the search criteria "computer memory system" AND (PUBYEAR > 2019-2023), a thorough review of all publications published between 2019 and 2023 was conducted in the Web of Science database and IEEE Xplore database. The results were reported in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) standards. In the instance of Web of Science, the database searches yielded a total of 28, 423 results, and 98,142 results in the case of IEEE Xplore. After reading the papers' abstracts, 126,263 search results were eliminated since they didn't fit the criteria. The remaining 302 articles were considered. A total of 32 studies were chosen for inclusion in the review after applying inclusion and exclusion criteria. The thorough analysis outlines the current state of computer memory systems as well as any new trends. Additionally, the report outlines prospective research goals and avenues for computer memory systems research.
Non-Volatile Memory (NVM), Quantum Memory, Neuromorphic Memory, Computer Memory System, Memory Hierarchy, 3D XPoint, Resistive RAM (ReRAM), Persistent Memory (PMEM)
Victor Worlanyo Gbedawo, Gideon Agyeman Owusu, Carl Komla Ankah, Mohammed Ibrahim Daabo. (2023). An Overview of Computer Memory Systems and Emerging Trends. American Journal of Electrical and Computer Engineering, 7(2), 19-26. https://doi.org/10.11648/j.ajece.20230702.11
Copyright © 2023 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
1. | Ali, M., Roy, S., Saxena, U., Sharma, T., Raghunathan, A., & Roy, K. (2022). Compute-in-Memory Technologies and Architectures for Deep Learning Workloads. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 30 (11), 1615–1630. https://doi.org/10.1109/TVLSI.2022.3203583. |
2. | Carmean, D., Ceze, L., Seelig, G., Stewart, K., Strauss, K., & Willsey, M. (2019). DNA Data Storage and Hybrid Molecular–Electronic Computing. Proceedings of the IEEE, 107 (1), 63–72. https://doi.org/10.1109/JPROC.2018.2875386. |
3. | Choe, W., Kim, J., & Ahn, J. (2020). A Study of Memory Placement on Hardware-Assisted Tiered Memory Systems. IEEE Computer Architecture Letters, 19 (2), 122–125. https://doi.org/10.1109/LCA.2020.3015613. |
4. | Du, Z., Zhang, Q., Lin, M., Li, S., Li, X., & Ju, L. (2023). A Comprehensive Memory Management Framework for CPU-FPGA Heterogenous SoCs. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 42 (4), 1058–1071. https://doi.org/10.1109/TCAD.2022.3179323. |
5. | Haddaway, N. R., Page, M. J., Pritchard, C. C., & McGuinness, L. A. (2022). |
6. | PRISMA2020: An R package and Shiny app for producing PRISMA 2020-compliant flow diagrams, with interactivity for optimised digital transparency and Open Synthesis. Campbell Systematic Reviews, 18 (2), e1230. https://doi.org/10.1002/cl2.1230. |
7. | Hosseini, M. S., Ebrahimi, M., Yaghini, P., & Bagherzadeh, N. (2022). Near Volatile and Non-Volatile Memory Processing in 3D Systems. IEEE Transactions on Emerging Topics in Computing, 10 (3), 1657–1664. https://doi.org/10.1109/TETC.2021.3115495. |
8. | Le Gallo, M., & Sebastian, A. (2020). An overview of phase-change memory device physics. Journal of Physics D: Applied Physics, 53 (21), 213002. https://doi.org/10.1088/1361-6463/ab7794. |
9. | Nair, R. (2015). Evolution of Memory Architecture. Proceedings of the IEEE, 103 (8), 1331–1345. https://doi.org/10.1109/JPROC.2015.2435018. |
10. | Rajendran, B., & Alibart, F. (2016). Neuromorphic Computing Based on Emerging Memory Technologies. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 6 (2), 198–211. https://doi.org/10.1109/JETCAS.2016.2533298. |
11. | Ranjan, A., Raha, A., Raghunathan, V., & Raghunathan, A. (2020). Approximate Memory Compression. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 28 (4), 980–991. https://doi.org/10.1109/TVLSI.2020.2970041. |
12. | Rizvi, S. S., & Chung, T.-S. (2010). Flash SSD vs HDD: High performance oriented modern embedded and multimedia storage systems. 2010 2nd International Conference on Computer Engineering and Technology, V7-297-V7-299. https://doi.org/10.1109/ICCET.2010.5485421. |
13. | Tang, B., Veluri, H., Li, Y., Yu, Z. G., Waqar, M., Leong, J. F., Sivan, M., Zamburg, E., Zhang, Y.-W., Wang, J., & Thean, A. V.-Y. (2022). Wafer-scale solution-processed 2D material analog resistive memory array for memory-based computing. Nature Communications, 13 (1), 3037. https://doi.org/10.1038/s41467-022-30519-w. |
14. | Upadhyay, N. K., Jiang, H., Wang, Z., Asapu, S., Xia, Q., & Yang, J. J. (2019). Emerging Memory Devices for Neuromorphic Computing. ADVANCED MATERIALS TECHNOLOGIES, 4 (4). https://doi.org/10.1002/admt.201800589. |
15. | Wen, F., Qin, M., Gratz, P. V., & Reddy, A. L. N. (2020). Hardware Memory Management for Future Mobile Hybrid Memory Systems. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 39 (11), 3627–3637. https://doi.org/10.1109/TCAD.2020.3012213. |
16. | Wen, H., & Zhang, W. (2018). Exploiting GPU with 3D Stacked Memory to Boost Performance for Data-Intensive Applications. 2018 IEEE High Performance Extreme Computing Conference (HPEC), 1–6. https://doi.org/10.1109/HPEC.2018.8547545. |
17. | Zhang, C., Sun, H., Li, S., Wang, Y., Chen, H., & Liu, H. (2023). A Survey of Memory-Centric Energy Efficient Computer Architecture. IEEE Transactions on Parallel and Distributed Systems, 34 (10), 2657–2670. https://doi.org/10.1109/TPDS.2023.3297595. |