A Lightweight Mitigation Technique for Resource-constrained Devices Executing DNN Inference Models under Neutron Radiation

Autor: Jonas Gava, Alex Hanneman, Geancarlo Abich, Rafael Garibotti, Sergio Cuenca-Asensi, Rodrigo Possamai Bastos, Ricardo Reis, Luciano Ost
Přispěvatelé: Universidad de Alicante. Departamento de Tecnología Informática y Computación, UniCAD: Grupo de investigación en CAD/CAM/CAE de la Universidad de Alicante
Rok vydání: 2023
Předmět:
Zdroj: IEEE Transactions on Nuclear Science. :1-1
ISSN: 1558-1578
0018-9499
Popis: Deep neural network (DNN) models are being deployed in safety-critical embedded devices for object identification, recognition, and even trajectory prediction. Optimised versions of such models, in particular the convolutional ones, are becoming increasingly common in resource-constrained edge-computing devices (e.g., sensors, drones), which typically rely on reduced memory footprint, low power budget and low-performance microprocessors. DNN models are prone to radiation-induced soft errors, and tackling their occurrence in resource-constrained devices is a mandatory and substantial challenge. While traditional replication-based soft error mitigation techniques will likely account for a reasonable performance penalty, hardware solutions are even more costly. To undertake this almost contradictory challenge, this work evaluates the efficiency of a lightweight software-based mitigation technique, called Register Allocation Technique (RAT), when applied to a convolutional neural network (CNN) model running on two commercial Arm microprocessors (i.e., Cortex-M4 and M7) under the effects of neutron radiation. Gathered results obtained from two neutron radiation campaigns suggest that RAT can reduce the number of critical faults in the CNN model running on both Arm Cortex-M microprocessors. Results also suggest that the SDC FIT rate of the RAT-hardened CNN model can be reduced in up to 83% with a runtime overhead of 32%. This work was partially funded by: CAPES; CNPq (317087/2021-5); FAPERGS (22/2551-0000570-5); UK EPSRC (EP/R513088/1); MultiRad (PAI project funded by Région Auvergne-Rhône-Alpes); IRT Nanoelec (ANR-10-AIRT-05 project funded by French PIA); UGA/LPSC/GENESIS platform; and PID2019-106455GB-C22 (funded by the Spanish Ministry of Science and Innovation).
Databáze: OpenAIRE
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