Skip to main content
SpringerLink
Log in

Computational Storage for 3D NAND Flash Error Recovery Flow Prediction

  • Conference paper
  • First Online:
Proceedings of SIE 2023 (SIE 2023)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1113))

Included in the following conference series:

  • 304 Accesses

Abstract

The Computational Storage paradigm is attracting increasing interest in many applications because of the performance and the energy-efficiency improvement, given by the tight coupling of processing elements with Solid State Drives through proper interconnection fabrics. In this work, we study a computational storage architecture aimed to boost the inference step of an Artificial Neural Network designed to predict the Error Recovery Flow outcome from the 3D NAND Flash memories characterization data. The application has been implemented on the Xilinx Alveo U250 Data center accelerator using a 15 bits fixed point precision, proving a 98.6% prediction accuracy, a performance boost up to 53.5\(\times \), and two orders of magnitude energy consumption reduction with respect to a CPU-only implementation.

This work has been partially supported by the grant UNIFE-FIR-2020, by the grant POR-FESR 2014–2020 TECH FAST LOMBARDIA - CUP E99J21008830007, and by the Spoke 1 “FutureHPC & BigData” of the Italian Research Center on High-Performance Computing, Big Data and Quantum Computing (ICSC) funded by MUR Missione 4 - Next Generation EU (NGEU).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: a next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019). https://doi.org/10.1145/3292500.3330701

  2. Calore, E., Schifano, S.F.: Performance assessment of FPGAs as HPC accelerators using the FPGA empirical roofline. In: 31th International Conference on Field-Programmable Logic and Applications (FPL), pp. 83–90 (2021). https://doi.org/10.1109/FPL53798.2021.00022

  3. Chollet, F., et al.: Keras (2015). https://keras.io

  4. Duarte, J., et al.: Fast inference of deep neural networks in FPGAs for particle physics. J. Instrument. 13(07), P07027 (2018). https://doi.org/10.1088/1748-0221/13/07/p07027

  5. Goda, A.: Recent progress on 3D NAND flash technologies. Electronics 10(24) (2021). https://doi.org/10.3390/electronics10243156

  6. He, R., Hu, H., Xiong, C., Han, G.: Artificial neural network assisted error correction for MLC NAND flash memory. Micromachines (Basel) 12(8) (2021). https://doi.org/10.3390/mi12080879

  7. HeydariGorji, A., Torabzadehkashi, M., Rezaei, S., Bobarshad, H., Alves, V., Chou, P.H.: Stannis: low-power acceleration of DNN training using computational storage devices. In: ACM/IEEE Design Automation Conference (DAC), pp. 1–6 (2020). https://doi.org/10.1109/DAC18072.2020.9218687

  8. Lee, J.K., Ko, K., Shin, H.: Analysis on process variation effect of 3D NAND flash memory cell through machine learning model. In: IEEE Electron Devices Technology Manufacturing Conference (EDTM), pp. 1–4 (2020). https://doi.org/10.1109/EDTM47692.2020.9117940

  9. Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 4768–4777 (2017). https://doi.org/10.5555/3295222.3295230

  10. Mielke, N.R., Frickey, R.E., Kalastirsky, I., Quan, M., Ustinov, D., Vasudevan, V.J.: Reliability of solid-state drives based on NAND flash memory. Proc. IEEE 105(9), 1725–1750 (2017). https://doi.org/10.1109/JPROC.2017.2725738

    Article  Google Scholar 

  11. Nakamura, T., Deguchi, Y., Takeuchi, K.: 9.1\(\times \) error acceptable adaptive artificial neural network coupled LDPC ECC for charge-trap and floating-gate 3D-NAND flash memories. In: IEEE Custom Integrated Circuits Conference (CICC), pp. 1–4 (2018). https://doi.org/10.1109/CICC.2018.8357064

  12. Nakamura, Y., Iwasaki, T., Takeuchi, K.: Machine learning-based proactive data retention error screening in 1Xnm TLC NAND flash. In: IEEE International Reliability Physics Symposium (IRPS), pp. PR-3-1–PR-3-4 (2016). https://doi.org/10.1109/IRPS.2016.7574632

  13. NVM Express WG: NVM Express Base Specification 2.0b (2022)

    Google Scholar 

  14. PCI-SIG: PCI Express Base Specification Revision 3.0 (2010)

    Google Scholar 

  15. Reed, D.A., Dongarra, J.: Exascale computing and big data. Commun. ACM 58, 56–68 (2015). https://doi.org/10.1145/2699414

  16. Samsung: SmartSSD Computational Storage Drive. https://www.xilinx.com/applications/data-center/computational-storage/smartssd.html

  17. SNIA: SNIA Computational Storage Technical Work Group (TWG). https://www.snia.org/computational (2019)

  18. Torabzadehkashi, M., Rezaei, S., HeydariGorji, A., Bobarshad, H., Alves, V., Bagherzadeh, N.: Computational storage: an efficient and scalable platform for big data and HPC applications. J. Big Data 6(1), 1–29 (2019). https://doi.org/10.1186/s40537-019-0265-5

    Article  Google Scholar 

  19. Zambelli, C., Bertaggia, R., Zuolo, L., Micheloni, R., Olivo, P.: Enabling computational storage through FPGA neural network accelerator for enterprise SSD. IEEE Trans. Circ. Syst. II: Express Briefs 66(10), 1738–1742 (2019). https://doi.org/10.1109/TCSII.2019.2929288

  20. Zambelli, C., et al.: Characterization of TLC 3D-NAND Flash endurance through machine learning for LDPC code rate optimization. In: IEEE International Memory Workshop (IMW), pp. 1–4 (2017). https://doi.org/10.1109/IMW.2017.7939074

  21. Zambelli, C., Ferro, E., Crippa, L., Micheloni, R., Olivo, P.: Dynamic V\(_{TH}\) tracking for cross-temperature suppression in 3D-TLC NAND flash. In: IEEE Int. Integrated Reliability Workshop (IIRW), pp. 1–4 (2019). https://doi.org/10.1109/IIRW47491.2019.8989886

  22. Zambelli, C., Marelli, A., Micheloni, R., Olivo, P.: Modeling the endurance reliability of intradisk RAID solutions for Mid-1X TLC NAND flash solid-state drives. IEEE Trans. Dev. Mater. Reliabil. 17(4), 713–721 (2017). https://doi.org/10.1109/TDMR.2017.2749639

    Article  Google Scholar 

  23. Zuolo, L., Zambelli, C., Marelli, A., Micheloni, R., Olivo, P.: LDPC soft decoding with improved performance in 1X–2X MLC and TLC NAND flash-based solid state drives. IEEE Trans. Emerg. Topics Comput. 7(3), 507–515 (2019). https://doi.org/10.1109/TETC.2017.2688079

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Università degli Studi di Ferrara, via G. Saragat 1, 44122, Ferrara, Italy

    Cristian Zambelli, Andrea Miola & Sebastiano Fabio Schifano

  2. Istituto Nazionale di Fisica Nucleare (INFN), 44122, Ferrara, Italy

    Andrea Miola, Enrico Calore & Sebastiano Fabio Schifano

  3. Avaneidi srl, 21047, Saronno, (VA), Italy

    Rino Micheloni

Authors
  1. Cristian Zambelli
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrea Miola
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Enrico Calore
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rino Micheloni
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sebastiano Fabio Schifano
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cristian Zambelli .

Editor information

Editors and Affiliations

  1. Department of Engineering, University of Messina, Messina, Italy

    Carmine Ciofi

  2. Dipartimento di Ingegneria Elettronica, University of Rome Tor Vergata, Rome, Italy

    Ernesto Limiti

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zambelli, C., Miola, A., Calore, E., Micheloni, R., Schifano, S.F. (2024). Computational Storage for 3D NAND Flash Error Recovery Flow Prediction. In: Ciofi, C., Limiti, E. (eds) Proceedings of SIE 2023. SIE 2023. Lecture Notes in Electrical Engineering, vol 1113. Springer, Cham. https://doi.org/10.1007/978-3-031-48711-8_51

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-48711-8_51

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-48710-1

  • Online ISBN: 978-3-031-48711-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation