Resource management strategies for many-core systems dictate the sharing of resources among applications such as power, processing cores, and memory bandwidth in order to achieve system goals. System goals require consideration of both system constraints (e.g., power envelope) and user demands (e.g., response time, energy-efficiency). Existing approaches use heuristics, control theory, and machine learning for resource management. They all depend on static system models, requiring a priori knowledge of system dynamics, and are therefore too rigid to adapt to emerging workloads or changing system dynamics.

This paper deals with challenges and possible solutions for incorporating self-awareness principles in EDA design flows for autonomous systems. We present a holistic approach that enables self-awareness across the software/hardware stack, from systems-on-chip to systems-of-systems (autonomous car) contexts. We use the Information Processing Factory (IPF) metaphor as an exemplar to show how self-awareness can be achieved across multiple abstraction levels, and discuss new research challenges. The IPF approach represents a paradigm shift in platform design by envisioning the move towards a consequent platform-centric design in which the combination of self-organizing learning and formal reactive methods guarantee the applicability of such cyber-physical systems in safety-critical and high-availability applications.

Future cyber-physical systems will host a large number of coexisting distributed applications on hardware platforms with thousands to millions of networked components communicating over open networks. These applications and networks are subject to continuous change. The current separation of design process and operation in the field will be superseded by a life-long design process of adaptation, infield integration, and update. Continuous change and evolution, application interference, environment dynamics and uncertainty lead to complex effects which must be controlled to serve a growing set of platform and application needs. Self-adaptation based on self-awareness and self-configuration has been proposed as a basis for such a continuous in-field process. Research is needed to develop automated in-field design methods and tools with the required safety, availability, and security guarantees. The paper shows two complementary use cases of self-awareness in architectures, methods, and tools for cyber-physical systems. The first use case focuses on safety and availability guarantees in self-aware vehicle platforms. It combines contracting mechanisms, tool based self-analysis and self-configuration. A software architecture and a runtime environment executing these tools and mechanisms autonomously are presented including aspects of self-protection against failures and security threats. The second use case addresses variability and long term evolution in networked MPSoC integrating hardware and software mechanisms of surveillance, monitoring, and continuous adaptation. The approach resembles the logistics and operation principles of manufacturing plants which gave rise to the metaphoric term of an Information Processing Factory that relies on incremental changes and feedback control. Both use cases are investigated by larger research groups. Despite their different approaches, both use cases face similar design and design automation challenges which will be summarized in the end. We will argue that seemingly unrelated research challenges, such as in machine learning and security, could also profit from the methods and superior modeling capabilities of self-aware systems.