The System-on-Chip revolution has not impacted the field of safety-critical systems as widely as the rest of the electronics market. However, it is nowadays acknowledged that sharing resources between applications of different criticality is a key leverage to reduce costs and improve performance. Certification of systems hosting such mixed-criticality application sets requires sufficient isolation between criticality levels, i.e. a low-critical task must not cause a fault in a high-critical task, especially a temporal fault, which is likely when several co-running tasks compete for a shared resource. This digest describes implementation schemes which can be included, at both hardware and software levels, in mixed-critical systems to enforce such isolation.

The role of smart and autonomous systems is becoming vital in many areas of industry and society. Expectations from such systems continuously rise and become more ambitious: long lifetime, high reliability, high performance, energy efficiency, and adaptability, particularly in the presence of changing environments. Computational self-awareness promises a comprehensive assessment of the system state for sensible and well-informed actions and resource management. Computational self-awareness concepts can be used in many applications such as automated manufacturing plants, telecommunication systems, autonomous driving, traffic control, smart grids, and wearable health monitoring systems. Developing self-aware systems from scratch for each application is the most common practice currently, but this is highly redundant, inefficient, and uneconomic. Hence, we propose a framework that supports modeling and evaluation of various self-aware concepts in hierarchical agent systems, where agents are made up of self-aware functionalities. This paper presents the Research on Self-Awareness (RoSA) framework and its design principles. In addition, self-aware functionalities abstraction, data reliability, and confidence, which are currently provided by RoSA, are described. Potential use cases of RoSA are discussed. Capabilities of the proposed framework are showcased by case studies from the fields of healthcare and industrial monitoring. We believe that RoSA is capable of serving as a common framework for self-aware modeling and applications and thus helps researchers and engineers in exploring the vast design space of hierarchical agent-based systems with computational self-awareness.

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The Complexity of emerging multi/many-core architectures and diversity of modern workloads demands coordinated dynamic resource management methods. We introduce a classification for these methods capturing the utilized resources and metrics. In this work, we use this classification to survey the key efforts in dynamic resource management.We first cover heuristic and optimization methods used to manage resources such as power, energy, temperature, Qualityof-Service (QoS) and reliability of the system. We then identify some of the machine learning based methods used in tuning architectural parameters in computer systems. In many cases, resource managers need to enforce design constraints during runtime with a certain level of guarantee. Hence, we also study the trend in deploying formal control theoretic approaches in order to achieve efficient and robust dynamic resource management.

Embedded systems have proliferated in various consumer and industrial applications with the evolution of Cyber-Physical Systems and the Internet of Things. These systems are subjected to stringent constraints so that embedded software must be optimized for multiple objectives simultaneously, namely reduced energy consumption, execution time, and code size. Compilers offer optimization phases to improve these metrics. However, proper selection and ordering of them depends on multiple factors and typically requires expert knowledge. State-ofthe-art optimizers facilitate different platforms and applications case by case, and they are limited by optimizing one metric at a time, as well as requiring a time-consuming adaptation for different targets through dynamic profiling.To address these problems, we propose the novel MLComp methodology, in which optimization phases are sequenced by a Reinforcement Learning-based policy. Training of the policy is supported by Machine Learning-based analytical models for quick performance estimation, thereby drastically reducing the time spent for dynamic profiling. In our framework, different Machine Learning models are automatically tested to choose the best-fitting one. The trained Performance Estimator model is leveraged to efficiently devise Reinforcement Learning-based multi-objective policies for creating quasioptimal phase sequences.Compared to state-of-the-art estimation models, our Performance Estimator model achieves lower relative error (< 2%) with up to 50× faster training time over multiple platforms and application domains. Our Phase Selection Policy improves execution time and energy consumption of a given code by up to 12% and 6%, respectively. The Performance Estimator and the Phase Selection Policy can be trained efficiently for any target platform and application domain.