As transformer-based object detection models progress, their impact in
critical sectors like autonomous vehicles and aviation is expected to grow.
Soft errors causing bit flips during inference have significantly impacted DNN
performance, altering predictions. Traditional range restriction solutions for
CNNs fall short for transformers. This study introduces the Global Clipper and
Global Hybrid Clipper, effective mitigation strategies specifically designed
for transformer-based models. It significantly enhances their resilience to
soft errors and reduces faulty inferences to ~ 0\%. We also detail extensive
testing across over 64 scenarios involving two transformer models (DINO-DETR
and Lite-DETR) and two CNN models (YOLOv3 and SSD) using three datasets,
totalling approximately 3.3 million inferences, to assess model robustness
comprehensively. Moreover, the paper explores unique aspects of attention
blocks in transformers and their operational differences from CNNs.

通过引入全局剪贴板和全局混合剪贴板的有效缓解策略，该研究旨在增强基于 Transformer 模型的鲁棒性，减少软错误导致的错误推断，并对注意力块在 Transformer 中的独特方面及其与 CNNs 的操作差异进行了详细研究。

全球 Clipper：增强基于 Transformer 的目标检测模型的安全性和可靠性

Global Clipper: Enhancing Safety and Reliability of Transformer-based  Object Detection Models

In contemporary times, the increasing complexity of the system poses
significant challenges to the reliability, trustworthiness, and security of the
SACRES. Key issues include the susceptibility to phenomena such as
instantaneous voltage spikes, electromagnetic interference, neutron strikes,
and out-of-range temperatures. These factors can induce switch state changes in
transistors, resulting in bit-flipping, soft errors, and transient corruption
of stored data in memory. The occurrence of soft errors, in turn, may lead to
system faults that can propel the system into a hazardous state. Particularly
in critical sectors like automotive, avionics, or aerospace, such malfunctions
can have real-world implications, potentially causing harm to individuals.
This paper introduces a novel fault injector designed to facilitate the
monitoring, aggregation, and examination of micro-architectural events. This is
achieved by harnessing the microprocessor's PMU and the debugging interface,
specifically focusing on ensuring the repeatability of fault injections. The
fault injection methodology targets bit-flipping within the memory system,
affecting CPU registers and RAM. The outcomes of these fault injections enable
a thorough analysis of the impact of soft errors and establish a robust
correlation between the identified faults and the essential timing
predictability demanded by SACRES.

本文介绍了一种用于监测、聚合和检查微架构事件的新型故障注入器，通过利用微处理器的 PMU 和调试接口，重点确保故障注入的重复性，与 SACRES 所需的关键时间可预测性之间建立了强大的相关性，从而实现对软错误的影响的全面分析。

一种微体系结构事件感知的实时嵌入式系统故障注入器

A Micro Architectural Events Aware Real-Time Embedded System Fault  Injector

Object detection neural network models need to perform reliably in highly
dynamic and safety-critical environments like automated driving or robotics.
Therefore, it is paramount to verify the robustness of the detection under
unexpected hardware faults like soft errors that can impact a systems
perception module. Standard metrics based on average precision produce model
vulnerability estimates at the object level rather than at an image level. As
we show in this paper, this does not provide an intuitive or representative
indicator of the safety-related impact of silent data corruption caused by bit
flips in the underlying memory but can lead to an over- or underestimation of
typical fault-induced hazards. With an eye towards safety-related real-time
applications, we propose a new metric IVMOD (Image-wise Vulnerability Metric
for Object Detection) to quantify vulnerability based on an incorrect
image-wise object detection due to false positive (FPs) or false negative (FNs)
objects, combined with a severity analysis. The evaluation of several
representative object detection models shows that even a single bit flip can
lead to a severe silent data corruption event with potentially critical safety
implications, with e.g., up to (much greater than) 100 FPs generated, or up to
approx. 90% of true positives (TPs) are lost in an image. Furthermore, with a
single stuck-at-1 fault, an entire sequence of images can be affected, causing
temporally persistent ghost detections that can be mistaken for actual objects
(covering up to approx. 83% of the image). Furthermore, actual objects in the
scene are continuously missed (up to approx. 64% of TPs are lost). Our work
establishes a detailed understanding of the safety-related vulnerability of
such critical workloads against hardware faults.

本文提出了一种新的度量标准 IVMOD（Image-wise Vulnerability Metric for Object Detection），用来评估图像级别上的目标检测漏报或误报对模型安全性的影响，研究表明硬件错误（如单个位翻转）会导致模型产生严重的数据损坏事件，该工作详细阐述了此类关键工作负载受硬件故障影响的安全漏洞。