Speaker: Sergio Repetto

Abstract: Functional safety is a foundational requirement of railway systems, which operate in highly critical environments and are governed by a rigorous European regulatory framework. Embedded railway systems—ranging from signalling and interlocking equipment to field devices, supervision, and diagnostic platforms—are designed according to well‑established principles such as fail‑safe behaviour, determinism, verifiability, and systematic risk management, as defined by the CENELEC standards. This lecture introduces the regulatory context, safety‑critical design principles, architectures, and lifecycle processes for development, verification, validation, and acceptance. It also discusses diagnostic and maintenance-oriented examples from railway R&D, and concludes with perspectives on software complexity and future interactions between embedded systems and AI, noting that AI is currently not permitted in railway safety-critical systems under current CENELEC regulations.

Speaker: Andrea Bondavalli

Abstract: Machine Learning (ML) is increasingly embedded in safety- and security-critical systems, from industrial IoT to autonomous platforms. Traditional ML focuses on accuracy and offers limited guarantees in real-world settings where misclassifications can have severe consequences. This lecture introduces a dependability-oriented perspective, shifting focus from accuracy to trustworthiness. It presents fail-controlled classifiers that manage uncertainty by rejecting low-confidence predictions, converting potentially dangerous misclassifications into controlled omission failures. Building on this, the lecture discusses redundancy strategies inspired by classical fault-tolerant design, including ensembles of self-checking classifiers that improve availability while preserving safety constraints. Applications to intrusion detection and time-dependent monitoring are also covered, emphasizing uncertainty-aware detection and temporal metrics such as detection latency, and providing a unified framework for integrating ML components into dependable critical systems.

Speaker: Mario Barbareschi

Abstract: As machine learning capabilities move from centralized cloud infrastructures toward distributed and resource-constrained platforms, edge intelligence is emerging as a critical paradigm in modern computing systems. This transition brings opportunities in responsiveness, privacy, and reduced dependence on continuous connectivity, while also introducing major challenges in efficiency, scalability, and trustworthiness. The lecture offers an overview of the main issues in bringing machine learning to the edge, focusing on the balance between performance and resource usage, and on the growing role of interpretable models for dependable decision-making. It also presents a unified system-level view of edge machine learning across algorithms, architectures, and application requirements.

Speaker: Matteo Sonza Reorda

Abstract: The widespread adoption of Edge AI systems in many application domains has been made possible by computationally powerful circuits (e.g., AI accelerators) manufactured with advanced semiconductor technologies. To meet application requirements, these circuits must operate correctly and remain free from faults across both manufacturing and operational phases. The complexity of modern circuits and the software running on them (often neural-network based) makes this goal highly challenging. This lecture introduces key concepts and terminology, then summarizes the main techniques used to reduce the probability that faulty circuits reach deployment and to mitigate the effects of faults occurring in the field.

Speaker: Luigi De Simone

Abstract: Industrial cloud-to-edge platforms increasingly consolidate mixed-criticality applications, yet mainstream cloud technologies often lack the isolation and robustness required in safety- and latency-sensitive domains. This lecture introduces cross-layer containment as a unifying framework to limit temporal and spatial interference, fault propagation, and latency variability across cloud-to-edge systems. It discusses mechanisms spanning runtime isolation, virtualization, and orchestration, including heterogeneous virtualization support, fault-injection techniques for robustness assessment, and latency-aware control-plane design. The session concludes with open research challenges and implications for safety, security, and embedded AI in next-generation edge-cloud platforms.