Reliability and Security Assessment of Modern Embedded Devices

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Nutrition and dietary intake and their association with mortality and hospitalization in adults with chronic kidney disease treated with hemodialysis

Adults receiving hemodialysis still experience high mortality rates. Several interventions that address the typical cardiovascular risk factors which are almost universally present in people with ESKD have been introduced. These interventions unfortunately do not significantly improve health outcomes in such populations. Nutrition and dietary patterns are potential factors influencing health in other health settings but poorly explored in the setting of ESKD. The aim of this body of work was to evaluate the association between exposure to different nutrients and dietary patterns and the risk of mortality (all-cause and cause-specific) and hospital admissions (any, and cause-specific) in adults with chronic kidney disease and specifically those with or end stage kidney disease receiving hemodialysis for renal replacement therapy. This project focused on understanding the impact of diet and nutrient intake, on CKD and ESKD through a comprehensive and systematic series of literature reviews and the design and conduct of the first large scale multinational primary cohort study to explore the association between nutrition (dietary intake) and clinical adverse events in the setting of hemodialysis

On the detection of always-on hardware trojans supported by a pre-silicon verification methodology

Hardware-based vulnerabilities are becoming a serious threat in the Integrated Circuit (IC) industry. Current System-on-Chip (SoC) designs are comprised of many Intellectual Property (IP) blocks coming from third-party vendors. These can maliciously insert additional hardware, commonly known as Hardware Trojans, aiming at degrading performance, altering functionality or even leaking secret information. According to their activation mechanism, Hardware Trojans are classified as triggered or always-on. While the detection approaches for the first class are widely explored even during the early stages of the IC design flow, the detection of always-on type mainly relies on side channel analyses, carried out after fabrication. This work presents a methodology oriented to detect always-on Hardware Trojans during the pre-silicon design stage. The proposed approach is able to detect suspicious intrusions by exploiting a signature mechanism developed during the RTL verification phase. The activity of carefully selected signals is spied to record and keep the state of the core. Finally, the efficacy of the technique has been validated on an open-source IP core with three different always-on Trojans

On the Reliability Assessment of Artificial Neural Networks Running on AI-Oriented MPSoCs

Nowadays, the usage of electronic devices running artificial neural networks (ANNs)-based applications is spreading in our everyday life. Due to their outstanding computational capabilities, ANNs have become appealing solutions for safety-critical systems as well. Frequently, they are considered intrinsically robust and fault tolerant for being brain-inspired and redundant computing models. However, when ANNs are deployed on resource-constrained hardware devices, single physical faults may compromise the activity of multiple neurons. Therefore, it is crucial to assess the reliability of the entire neural computing system, including both the software and the hardware components. This article systematically addresses reliability concerns for ANNs running on multiprocessor system-on-a-chips (MPSoCs). It presents a methodology to assign resilience scores to individual neurons and, based on that, schedule the workload of an ANN on the target MPSoC so that critical neurons are neatly distributed among the available processing elements. This reliability-oriented methodology exploits an integer linear programming solver to find the optimal solution. Experimental results are given for three different convolutional neural networks trained on MNIST, SVHN, and CIFAR-10. We carried out a comprehensive assessment on an open-source artificial intelligence-based RISC-V MPSoC. The results show the reliability improvements of the proposed methodology against the traditional scheduling

Evaluation and mitigation of faults affecting Swin Transformers

In the last decade, a huge effort has been spent on assessing the reliability of Convolutional Neural networks (CNNs), probably the most popular architecture for image classification tasks. However, modern Deep Neural Networks (DNNs) are rapidly overtaking CNNs, as state-of-the-art results for many tasks are achieved with the Transformers, innovative DNN models. Transformers' architecture introduces the concept of attention as an alternative to the classical convolution operation. The aim of this work is to propose a reliability analysis of the Swin Transformer, one of the most accurate DNN used for Image Classification, that greatly improves the results obtained by traditional CNNs. In particular, this paper shows that, similar to CNNs, Transformers are susceptible to single faults affecting weights and neurons. Furthermore, it is shown how output ranging, a well-known technique to reduce the impact of a fault in CNNs, is not as effective for the Transformer. The alternative solution proposed by this work is to introduce a ranging not only on the output, but also on the input and on the weight of the fully connected layers. Results show that, on average, the number of critical faults (i.e., that modify the network's output) affecting neurons decreases by a factor of 1.91, while for faults affecting the network's weights this value decreases by a factor of 1 * 10 ^ 5

Evaluating Convolutional Neural Networks Reliability depending on their Data Representation

Safety-critical applications are frequently based on deep learning algorithms. In particular, Convolutional Neural Networks (CNNs) are commonly deployed in autonomous driving applications to fulfil complex tasks such as object recognition and image classification. Ensuring the reliability of CNNs is thus becoming an urgent requirement since they constantly behave in human environments. A common and recent trend is to replace the full-precision CNNs to make way for more optimized models exploiting approximation paradigms such as reduced bit-width data type. If from one hand this is poised to become a sound solution for reducing the memory footprint as well as the computing requirements, it may negatively affect the CNNs resilience. The intent of this work is to assess the reliability of a CNN-based system when reduced bit-widths are used for the network parameters (i.e., synaptic weights). The approach evaluates the impact of permanent faults in CNNs by adopting several bit-width schemes and data types, i.e., floating-point and fixed-point. This determines the trade-off between the CNN accuracy and the bits required to represent network weights. The characterization is performed through a fault injection environment built on the darknet open source framework. Experimental results show the effects of permanent fault injections on the weights of LeNet-5 CNN

Open-Set Recognition: an Inexpensive Strategy to Increase DNN Reliability

Deep Neural Networks (DNNs) are nowadays widely used in low-cost accelerators, characterized by limited computational resources. These models, and in particular DNNs for image classification, are becoming increasingly popular in safety-critical applications, where they are required to be highly reliable. Unfortunately, increasing DNNs reliability without computational overheads, which might not be affordable in low-power devices, is a non-trivial task. Our intuition is to detect network executions affected by faults as outliers with respect to the distribution of normal network's output. To this purpose, we propose to exploit Open-Set Recognition (OSR) techniques to perform Fault Detection in an extremely low-cost manner. In particuar, we analyze the Maximum Logit Score (MLS), which is an established Open-Set Recognition technique, and compare it against other well-known OSR methods, namely OpenMax, energy-based out-of-distribution detection and ODIN. Our experiments, performed on a ResNet-20 classifier trained on CIFAR-10 and SVHN datasets, demonstrate that MLS guarantees satisfactory detection performance while adding a negligible computational overhead. Most remarkably, MLS is extremely convenient to configure and deploy, as it does not require any modification or re-training of the existing network. A discussion of the advantages and limitations of the analysed solutions concludes the paper

A Model-Based Framework to Assess the Reliability of Safety-Critical Applications

Solutions based on artificial intelligence and brain-inspired computations like Artificial Neural Networks (ANNs) are suited to deal with the growing computational complexity required by state-of-the-art electronic devices. Many applications that are being deployed using these computational models are considered safety-critical (e.g., self-driving cars), producing a pressing need to evaluate their reliability. Besides, state-of-the-art ANNs require significant memory resources to store their parameters (e.g., weights, activation values), which goes outside the possibility of many resource-constrained embedded systems. In this light, Approximate Computing (AxC) has become a significant field of research to improve memory footprint, speed, and energy consumption in embedded and high-performance systems. The use of AxC can significantly reduce the cost of ANN implementations, but it may also reduce the inherent resiliency of this kind of application. On this scope, reliability assessments are carried out by performing fault injection test campaigns. The intent of the paper is to propose a framework that, relying on the results of radiation tests in Commercial-Off-The-Shelf (COTS) devices, is able to assess the reliability of a given application. To this end, a set of different radiation-induced errors in COTS memories is presented. Upon these, specific fault models are extracted to drive emulation-based fault injections

Dietary patterns for adults with chronic kidney disease

This is the protocol for a review and there is no abstract. The objectives are as follows: This review will evaluate the benefits and harms of dietary patterns among adults with CKD (any stage including people with end-stage kidney disease (ESKD) treated with dialysis, transplantation or supportive care)