Silent Data Corruption (SDC) in processors can lead to various application-level issues, such as incorrect calculations and even data loss. Since traditional techniques are not effective in detecting these errors, it is very hard to address problems ...

Embedded devices play critical roles in security and safety, demanding robust protection against fault injection attacks. Among the myriad of fault effects, the instruction skip fault model stands out due to its recurrent manifestation in silicon ...

Erasure coding has been demonstrated as a storage-efficient means against failures, yet its tunability remains a challenging issue in data centers, which is prone to induce substantial cross-cluster traffic. In this article, we present ClusterRT, a ...

Voltage scaling is one of the most promising approaches for energy efficiency improvement but also brings challenges to fully guaranteeing stable operation in modern VLSI. To tackle such issues, we further extend the DependableHD to the second version ...

Systems offering fault-resilient, energy-efficient, soft real-time data communication have wide applications in Industrial Internet-of-Things (IIoT). While there have been extensive studies for fault resilience in real-time embedded systems, ...

Cyber-physical systems (CPS) are used in various safety-critical domains such as robotics, industrial manufacturing systems, and power systems. Faults and cyber attacks have been shown to cause safety violations, which can damage the system and endanger ...

Nowadays, several software systems rely on stream processing architectures to deliver scalable performance and handle large volumes of data in near real-time. Stream processing frameworks facilitate scalable computing by distributing the application's ...

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 ...

Hardware faults in AI accelerators, particularly in accelerator memory, can alter pre-trained deep neural network parameters, leading to errors that compromise performance. To address this, just-intime (JIT) fault detection and mitigation are crucial. ...

This study focuses on methods to improve the reliability of persistent memory systems. By utilizing Montage (ICPP'21), we identify areas that have potential for enhancement, particularly in relation to the vulnerability of data loss in certain failure ...

We consider the problem of supporting payment transactions in an asynchronous system in which up to f validators are subject to Byzantine failures under the control of an adaptive adversary. It was shown that, in the case of a single owner, this problem ...

Exascale machines have a small mean time between failures, necessitating fault tolerance. Out-of-the-box fault-tolerant solutions, such as checkpoint-restart and replication, apply to any algorithm but incur significant overhead costs. Long integer ...

Today's IoT applications exploit the capabilities of three different computation environments: sensors, edge, and cloud. Ensuring fault tolerance at the edge level presents unique challenges due to complex network hierarchies and the presence of resource-...

Microservice architecture is widely used in the development of distributed applications. Many problems present at the beginning of using this approach have already been solved. However, one of the fundamental problems that seriously affects the ...

Federated learning was introduced in 2016 by Google researchers to train machine learning models to preserve user privacy. Basically, applying the federated learning concept, the companies’ servers receive partially trained models instead of data from ...

Fault tolerance, energy management, and quality of service (QoS) are essential aspects for the design of real-time embedded systems. In this work, we focus on exploring methods that can simultaneously address the above three critical issues under standby-...

Federated Learning (FL) has emerged as a decentralised machine learning paradigm for distributed systems, particularly in edge and IoT environments. However, ensuring fault tolerance and self-recovery in such scenarios remains challenging, because of the ...

For real-time embedded systems, QoS (Quality of Service), fault tolerance, and energy budget constraint are among the primary design concerns. In this research, we investigate the problem of energy constrained standby-sparing for both periodic and ...

Compute-in-memory based binary neural networks or CiM-BNNs offer high energy/area efficiency for the design of edge deep neural network (DNN) accelerators, with only a mild accuracy reduction. However, for successful deployment, the design of CiM-BNNs ...

Byzantine fault-tolerant (BFT) consensus algorithms are at the core of providing safety and liveness guarantees for distributed systems that must operate in the presence of arbitrary failures. Recently, numerous new BFT algorithms have been proposed, not ...