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 The current separation of design process and operation in the field will be superseded by a life-long design process of adaptation, infield integration, and update.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.

This paper presents lattice-based Boolean diagrams (LBBDs), a graphical representation of Boolean functions that is not derived from binary decision diagrams (BDDs), as well as symbolic manipulation algorithms. It also identifies a class of Boolean functions where LBBDs are demonstrably more efficient to construct, and reason with, when compared to BDDs. The case studies include ITC99 and MCNC benchmarks, randomly generated cube covers or sum-of-products (SOP) formulas as well as multi-level Boolean formulas. Finally, LBBDs proved to be instrumental to the efficient runtime verification of software over distributed multiprocessor systems.

Runtime Verification (RV) has recently emerged as a complementary technology to extend coverage of conventional software verification methods. To address the substantial performance and power overhead of pure software RV frameworks, this paper introduces NUVA, which stands for nonuniform verification architecture, a distributed automata-based RV architecture for parametric specifications in the form of parameterized finite-state automata, with a case study over a cache-coherent nonuniform-memory-access (ccNUMA) multiprocessor. The core of NUVA is a coherent distributed automata transactional memory (ATM) that efficiently maintains states of a dynamic population of automata checkers organized into a rooted dynamic directed acyclic graph (DAG) concurrently shared among all processor nodes. A cycle-accurate model of a ccNUMA multiprocessor confirms that performance slowdown is 1~3% and NoC message traffic increases by 10~15% at parametric event density1of 0.025 EPI for two compute-intensive scientific benchmarks having irregular concurrent data structures. The detailed architecture and implementation metrics in TSMC 40nm CMOS technology are presented. NUVA can be dimensioned to incur average total power overhead of less than 140mW and area overhead of 4mm2for a quad-core multiprocessor chip, at operating frequency of 250MHz. It is also estimated to incur 1.9~2.6% area overhead and 0.5~1% power overhead when integrated with Intel's family of high-end desktop and mobile quad-core processors operating at 1.6~3.2GHz. Our silicon implementation achieves average performance2of 1.5 MEPS/mW. For a processor core with 5 MIPS/mW, this corresponds to a 3.2% drop in energy efficiency at parametric event density of 0.01 EPI.