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Asgari Khoshouyeh, Ali

University of British Columbia

Convolutional Neural Networks (CNNs) are broadly used in safety-critical applications such as autonomous vehicles. While demonstrating high accuracy, CNN models are vulnerable to Dynamic Random Access Memory (DRAM) errors corrupting their parameters, thereby degrading their accuracy. Unfortunately, existing techniques for protecting CNNs from memory errors are either costly or not complete, meaning that they fail to protect from large-granularity, multi-bit DRAM errors. In this thesis, we propose a software-implemented coding scheme, Structural Coding, which is able to achieve three orders of magnitude reduction in Silent Data Corruption (SDC) rates of CNNs under large-granularity memory errors. Its error correction coverage is also significantly higher than other software-techniques to protect CNNs from faults in the memory. Additionally, its average performance overhead on a real machine is less than 3%. The memory footprint overhead of Structural Coding is <27%, but can be reduced to 4.3% with application-specific tuning.

Text

2023-01-02

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/83530

Electrical and Computer Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/