Experimental evaluation of neutron-induced errors on a multicore RISC-V platform

TL;DR

This paper evaluates neutron-induced error rates on a RISC-V platform, revealing higher error rates during intensive tasks and analyzing the nature of errors and system interruptions.

Contribution

It provides the first detailed neutron radiation error analysis on a commercial RISC-V ASIC, highlighting error behaviors in safety-critical applications.

Findings

Error rate can be 3.2x higher during CNN classification tasks.

Most errors (96.12%) do not cause misclassification.

Application hangs are the main cause of system interruptions.

Abstract

RISC-V architectures have gained importance in the last years due to their flexibility and open-source Instruction Set Architecture (ISA), allowing developers to efficiently adopt RISC-V processors in several domains with a reduced cost. For application domains, such as safety-critical and mission-critical, the execution must be reliable as a fault can compromise the system's ability to operate correctly. However, the application's error rate on RISC-V processors is not significantly evaluated, as it has been done for standard x86 processors. In this work, we investigate the error rate of a commercial RISC-V ASIC platform, the GAP8, exposed to a neutron beam. We show that for computing-intensive applications, such as classification Convolutional Neural Networks (CNN), the error rate can be 3.2x higher than the average error rate. Additionally, we find that the majority (96.12%) of the errors on the CNN do not generate misclassifications. Finally, we also evaluate the events that cause application interruption on GAP8 and show that the major source of incorrect interruptions is application hangs (i.g., due to an infinite loop or a racing condition).