Convolutional Neural Networks (CNN), particularly those used in critical applications, such as autonomous driving, medical systems, and aerospace, require high reliability. While these algorithms exhibit inherent resilience, they remain sus-ceptible to Single-Event Effects (SEE) occurring at the hard-ware and impacting the model execution. These effects, usually induced by interactions with radiation particles, can lead to errors in electronic components, potentially causing incorrect inferences and increasing the risk of mispredictions. Meanwhile, quantization and pruning are widely employed to reduce the hardware footprint of CNN models, facilitating their deployment on embedded systems. Even when the models are reduced, CNN remain too large for an exhaustive fault injection campaign to assess their resilience. To address these challenges, we propose SFI4NN, a Statistical Fault Injection (SFI) framework specifically designed to evaluate the fault sensitivity of fixed-point quantized and pruned CNN architectures. Furthermore, we analyze the model resilience as a function of the pruning rate, showing that CNN sensitivity increases as pruning becomes more aggressive. The obtained results enable the development of hardware hardening strategies with reduced costs that are tailored to the reliability requirements of targeted applications. Experimental results demonstrate a 96 % improvement in resilience, with minimal hardware overhead compared to conventional hardening techniques such as triplication.

Embedded Neural Networks (NNs) face significant challenges due to Single-Event Upsets (SEUs), compromising their reliability. To address this challenge, previous works study SEU layers sensitivity of AI models. Contrary to these techniques, remaining at high level, we propose a more accurate analysis, highlighting that, except for the last layer, faults transitioning from 0 to 1 significantly impact classification outcomes. Based on this specific behavior, we propose a simple hardware block able to detect and mitigate the SEU impact. Obtained results show that HTAG protection efficiency is near 96.85% for the LeNet-5 CNN inference model, suitable for an embedded system. This result can be improved with other protection methods for the classification layer. Additionally, it significantly reduces area overhead and critical path compared to existing approaches.

Embedded neural networks are increasingly deployed in critical applications, such as avionics and autonomous vehicle control. However, their reliability is challenged by various sources of soft errors, including radiation-induced faults from cosmic ray strikes, leading to Single Event Upsets (SEUs). To ensure the reliability of such systems, we present a novel hardware-based fault protection strategy tailored for embedded neural networks. The idea is based on mitigating faults by adapting at run-time any erroneous values (parameters, intermediate data) due to SEU towards zero upon fault detection. As neural networks exhibit heterogeneous sensitivity to fault direction, our hardware-based approach triplicates the sign bit (TMR) and uses a Voter block based on logical AND/OR gates to handle fault directionality. Through a comprehensive and exhaustive fault injection study, conducted on a Convolutional Neural Network (CNN) model, implemented on FPGA using fixed-point quantization, we show that our method is applicable to various hardware architectures while optimizing hardware cost, a crucial aspect in the context of embedded systems. Obtained results show that VANDOR protection efficiency is near ${9 0 . 9 7 \%}$ for the LeNet-5 CNN inference model, suitable for an embedded system. Additionally, it significantly reduces area overhead compared to existing approaches.

Modern technologies make hardware designs more and more sensitive to radiation particles and related faults. As a result, analysing the behavior of a system under radiation-induced faults has become an essential part of the system design process. Existing approaches either focus on analysing the radiation impact at the lower hardware design layers, without further propagating any radiation-induced fault to the system execution, or analyse system reliability at higher hardware or application layers, based on fault models that are agnostic of the fabrication technology and the radiation environment. Flodam combines the benefits of existing approaches by providing a novel cross-layer reliability analysis from the semiconductor layer up to the application layer, able to quantify the risks of faults under a given context, taking into account the environmental conditions, the physical hardware design and the application under study.

Since several decades, fault tolerance has become a major research field due to transistor shrinking and core number increasing in system-on-chip (SoC). Especially, faults occurring to the network-on-chips (NoCs) of those systems have a significant impact, due to the high amount of data, crossing the NoC, for the communication among intellectual properties (IPs). Furthermore, existing fault-tolerant approaches cannot efficiently deal with several permanent faults, which occur in NoC routers. To address these limitations, we propose the bit shuffling method (BiSuT) for fault-tolerant NoCs that reduces the impact of faults on data communications. To achieve that, the proposed approach exploits, at runtime, the position of permanent faults and changes the order of bits inside a flit. Our method reduces, as much as possible, the impact of faults by transferring the faults on least significant bits (LSBs), instead of keeping them on most significant bits (MSBs). The results obtained by extensive evaluations show that BiSuT can reduce the impact of multiple permanent faults, with low hardware costs, compared to the existing approaches, like the Hamming code.

Since several decades, fault tolerance has become a major research field, due to transistor shrinking and core number increasing in System-on-Chip (SoC). Especially, faults occurring at the Network-on-Chips (NoCs) of those systems have a significant impact, since NoCs are the key component of on-chip communication. Several fault tolerant approaches have been proposed, which are, however, limited against multiple permanent faults. To reduce the impact of these faults on the data communications, we propose a bit-shuffling method for fault tolerant NoCs. The proposed approach exploits, at runtime, the position of the permanent faults and changes the order of bits inside a flit. Our bit-shuffling method reduces as much as possible the fault impact, by transferring the faults from Most Significant Bits (MSBs) towards Least Significant Bits (LSBs). With this technique, we show that, in presence of multiple permanent faults, the Mean Square Error (MSE) on the payload transmission is reduce from 1017to 105under three permanent fault for 32-bit unsigned integers. This technique also ensures the correct transmission of headers under multiple permanent faults.