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FeFET-based Binarized Neural Networks Under Temperature-dependent Bit Errors

Yayla, Mikail; Buschjager, Sebastian; Gupta, Aniket; Chen, Jian-Jia; Henkel, Jorg; Morik, Katharina; Chen, Kuan-Hsun; Amrouch, Hussam

Abstract:

Ferroelectric FET (FeFET) is a highly promising emerging non-volatile memory (NVM) technology, especially for binarized neural network (BNN) inference on the low-power edge. The reliability of such devices, however, inherently depends on temperature. Hence, changes in temperature during run time manifest themselves as changes in bit error rates. In this work, we reveal the temperature-dependent bit error model of FeFET memories, evaluate its effect on BNN accuracy, and propose countermeasures. We begin on the transistor level and accurately model the impact of temperature on bit error rates of FeFET. This analysis reveals temperature-dependent asymmetric bit error rates. Afterwards, on the application level, we evaluate the impact of the temperature-dependent bit errors on the accuracy of BNNs. Under such bit errors, the BNN accuracy drops to unacceptable levels when no countermeasures are employed. We propose two countermeasures: (1) Training BNNs for bit error tolerance by injecting bit flips into the BNN data, and (2) applying a bit error rate assignment algorithm (BERA) which operates in a layer-wise manner and does not inject bit flips during training. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000137051
Veröffentlicht am 12.09.2021
Originalveröffentlichung
DOI: 10.1109/TC.2021.3104736
Scopus
Zitationen: 10
Dimensions
Zitationen: 11
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Technische Informatik (ITEC)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 0018-9340, 1557-9956, 2326-3814
KITopen-ID: 1000137051
Erschienen in IEEE Transactions on Computers
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Band 71
Heft 7
Seiten 1681 - 1695
Vorab online veröffentlicht am 13.08.2021
Nachgewiesen in Scopus
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