In-Place Zero-Space Memory Protection for CNN

Conference · Sun Dec 01 04:00:00 EST 2019

OSTI ID:1606858

Guan, Hui ^[1]; Ning, Lin ^[2]; Lin, Zhen ^[2]; Shen, Xipeng ^[1]; Zhou, Huiyang ^[2]; Lim, Seung-Hwan ^[3]

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1. North Carolina State University
2. North Carolina State University (NCSU), Raleigh
3. ORNL

Convolutional Neural Networks (CNN) are being actively explored for safetycritical applications such as autonomous vehicles and aerospace, where it is essential to ensure the reliability of inference results in the presence of possible memory faults. Traditional methods such as error correction codes (ECC) and Triple Modular Redundancy (TMR) are CNN-oblivious and incur substantial memory overhead and energy cost. This paper introduces in-place zero-space ECC assisted with a new training scheme weight distribution-oriented training. The new method provides the first known zero space cost memory protection for CNNs without compromising the reliability offered by traditional ECC.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 1606858
• Country of Publication: United States
• Language: English