Deep learning (DL) reliability is becoming a growing concern, and efficient reliability assessment approaches are required to meet safety constraints. This article presents a survey of the main DL reliability assessment methodologies, focusing mainly on fault injection techniques used to evaluate DL resilience.

RISC-V is a novel open instruction set architecture that supports multiple platforms while maintaining simplicity and reliability. Despite its novelty, the software support for RISC-V has been increasing in the last years, given that popular toolchains and operating systems already have support for RISC-V. However, although many works have been exploring the RISC-V software ecosystem, no work that raised the current state of software support for RISC-V is available. In this context, this survey reviews the contributions introduced in the last years to understand the RISC-V’s software ecosystem and its usage in both academic and industrial environments. We classified and evaluated the works into four main categories: application fields, RISC-V implementations, software architecture, and deployment features. The primary goal of this research is to provide the community with a comprehensive overview of the current state of RISC-V software support and identify and highlight the main contributions from recent work.

Convolutional Neural Networks (CNNs) are currently one of the most widely used predictive models in machine learning. Recent studies have demonstrated that hardware faults induced by radiation fields, including cosmic rays, may significantly impact the CNN inference leading to wrong predictions. Therefore, ensuring the reliability of CNNs is crucial, especially for safety-critical systems. In the literature, several works propose reliability assessments of CNNs mainly based on statistically injected faults. This work presents a software emulator capable of injecting real faults retrieved from radiation tests. Specifically, from the device characterisation of a DRAM memory, we extracted event rates and fault models. The software emulator can reproduce their incidence and access their effect on CNN applications with a reliability assessment precision close to the physical one. Radiation-based physical injections and emulator-based injections are performed on three CNNs (LeNet-5) exploiting different data representations. Their outcomes are compared, and the software results evidence that the emulator is able to reproduce the faulty behaviours observed during the radiation tests for the targeted CNNs. This approach leads to a more concise use of radiation experiments since the extracted fault models can be reused to explore different scenarios (e.g., impact on a different application).

Recent research has shown interest in adopting the RISC-V processors for high-reliability electronics, such as aerospace applications. The openness of this architecture enables the implementation and customization of the processor features to increase their reliability. Studies on hardened RISC-V processors facing harsh radiation environments apply fault tolerance techniques in the processor core and peripherals, exploiting system redundancies. In prior work, we present a hardened RISC-V System-on-Chip (SoC), which could detect and correct radiation-induced faults with limited fault awareness. Therefore, in this work, we propose solutions to extend the fault observability of the SoC implementation by providing error detection and monitoring. For this purpose, we introduce observation features in the redundant structures of the system, enabling the report of valuable information that supports enhanced radiation testing and support the application to perform actions to recover from critical failures. Thus, the main contribution of this work is a solution to improve fault awareness and the analysis of the fault models in the system. In order to validate this solution, we performed complementary experiments in two irradiation facilities, comprehending atmospheric neutrons and a mixed-field environment, in which the system proved to be valuable for analyzing the radiation effects on the processor core and its peripherals. In these experiments, we were able to obtain a range of error reports that allowed us to gain a deeper understanding of the faults mechanisms, as well as improve the characterization of the SoC.