2846298 SzGeCERN 20230118203542.0 DOI IEEE 10.1109/RADECS53308.2021.9954496 oai:cds.cern.ch:2846298 cerncds:CERN https://inspirehep.net/api/oai2d 2023-01-17T05:00:12Z marcxml oai:inspirehep.net:2613770 2023-01-16T09:44:23Z Inspire 2613770 eng Giordano, Marco Zurich, ETH CERN and Intern at CERN IEEE General Purpose and Neural Network Approach for Benchmarking Microcontrollers Under Radiation 2021 8 p IEEE In this work a testing methodology for micro-controllers exposed to radiation is proposed. General purpose benchmarks are reviewed to provide a mean of testing all the macro-areas of a microcontroller, and a neural network benchmark is introduced as a representative class of novel computing algorithms for IoT devices. Metrics from literature are reviewed and a new metric, the Mean Energy per Unit Workload Between Failure, is introduced. It combines computing performance and energy consumption in a single unit, making it specifically useful to benchmark battery-operated edge nodes. A method to analyse reset causes is also introduced, giving important insights into failure mechanisms and potential patterns. The testing strategy has been validated on a representative class of four Cortex M0+ and Cortex M4 microcontrollers irradiated under a 200MeV proton beam with different fluences. Results from the irradiation campaign are presented and commented on to validate the benchmarks and metrics discussed. IEEE 2021 SzGeCERN Detectors and Experimental Techniques author Protons author Performance evaluation author Radiation effects author Particle beams author Microcontrollers author Neural networks author Failure analysis author Benchmark testing author Edge computing ARTICLE CERN Ferraro, Rudy CERN Magno, Michele Zurich, ETH Danzeca, Salvatore CERN 1-8 C21-09-13.12 2021 13 2823874 vienna20210913 1-8 ARTICLE ConferencePaper