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Lifetime Enhancement for RRAM-based Computing-In-Memory Engine Considering Aging and Thermal Effects

Publication ,  Conference
Zhang, S; Zhang, GL; Li, B; Li, HH; Schlichtmann, U
Published in: Proceedings - 2020 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2020
August 1, 2020

RRAM-based computing-in-memory engines provide a promising platform to accelerate deep neural networks. The programming process imposes high voltages onto the RRAM cells and thus degrades their valid conductance ranges from the fresh state, an effect called aging. Consequently, the expected conductances of RRAM cells corresponding to the weights after training may fall outside of the valid ranges, potentially leading to a significant accuracy degradation. In addition, an uneven temperature distribution due to different conductances accelerates the aging effect further. Moreover, the uneven temperatures can cause accuracy discrepancy between the tuning process and inference, thus reducing the lifetime of such accelerators even further. In this paper, we propose to counter aging and thermal effects by distributing aging stress and high-temperature RRAM cells evenly, during both software training and hardware mapping, to extend the lifetime of computing-in-memory engines. Experimental results demonstrate lifetime enhancement up to 453 times while maintaining the classification accuracy.

Duke Scholars

Published In

Proceedings - 2020 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2020

DOI

ISBN

9781728149226

Publication Date

August 1, 2020

Start / End Page

11 / 15
 

Citation

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Zhang, S., Zhang, G. L., Li, B., Li, H. H., & Schlichtmann, U. (2020). Lifetime Enhancement for RRAM-based Computing-In-Memory Engine Considering Aging and Thermal Effects. In Proceedings - 2020 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2020 (pp. 11–15). https://doi.org/10.1109/AICAS48895.2020.9073995
Zhang, S., G. L. Zhang, B. Li, H. H. Li, and U. Schlichtmann. “Lifetime Enhancement for RRAM-based Computing-In-Memory Engine Considering Aging and Thermal Effects.” In Proceedings - 2020 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2020, 11–15, 2020. https://doi.org/10.1109/AICAS48895.2020.9073995.
Zhang S, Zhang GL, Li B, Li HH, Schlichtmann U. Lifetime Enhancement for RRAM-based Computing-In-Memory Engine Considering Aging and Thermal Effects. In: Proceedings - 2020 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2020. 2020. p. 11–5.
Zhang, S., et al. “Lifetime Enhancement for RRAM-based Computing-In-Memory Engine Considering Aging and Thermal Effects.” Proceedings - 2020 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2020, 2020, pp. 11–15. Scopus, doi:10.1109/AICAS48895.2020.9073995.
Zhang S, Zhang GL, Li B, Li HH, Schlichtmann U. Lifetime Enhancement for RRAM-based Computing-In-Memory Engine Considering Aging and Thermal Effects. Proceedings - 2020 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2020. 2020. p. 11–15.

Published In

Proceedings - 2020 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2020

DOI

ISBN

9781728149226

Publication Date

August 1, 2020

Start / End Page

11 / 15