Specific features of the use of artificial intelligence in the development of the architecture of intelligent fault-tolerant radar systems

The problem of architecture development of modern radar systems using artificial intelligence technology is considered. The main difference is the use of a neural network in the form of a set of heterogeneous neuromultimicroprocessor modules, which are rebuilt in the process of solving the problem systematically in real time by the means of the operating system. This architecture promotes the implementation of cognitive technologies that take into account the requirements for the purpose, the influence of external and internal factors. The concept of resource in general and abstract resource of reliability in particular and its role in designing a neuromultimicroprocessor with fault tolerance properties is introduced. The variation of the ratio of performance and reliability of a fault-tolerant neuromultimicroprocessor of real time with a shortage of reliability resources at the system level by means of the operating system is shown, dynamically changing the architectural appearance of the system with structural redundancy, using fault-tolerant technologies and dependable computing.

Fault-Tolerant Computing with Heterogeneous Hardening Modes

Fault-tolerance using (full-scale) redundancy-based techniques has been employed to detect and correct reliability errors (i.e., soft errors), but they pose significant area and power overhead. On the other hand, due to the masking and the error tolerance properties at different system layers and of different applications, respectively, reliable heterogeneous architectures have been emerged as an attractive design choice for power-efficient dependable computing platforms. This chapter discusses the building blocks of such computing systems, based on both embedded and superscalar processors, with different reliability (fault-tolerant) modes at the architecture layer to memories like caches, for heterogeneous in-order and out-of-order processors. We provide a comprehensive reliability, i.e., soft error, vulnerability analysis of different components in in-order and out-of-order processors, e.g., caches. We also discuss different methodologies to improve the performance and power of such a system by analyzing these vulnerabilities. Moreover, we show how such heterogeneous hardware-level hardening modes can further be complemented by software-level techniques that can be realized using a reliability-driven compiler (as introduced in Chapter “Dependable Software Generation and Execution on Embedded Systems”).

Applying Text Analytics for Studying Research Trends in Dependability

The dependability of systems and networks has been the target of research for many years now. In the 1970s, what is now known as the top conference on dependability—The IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)—emerged gathering international researchers and sparking the interest of the scientific community. Although it started in niche systems, nowadays dependability is viewed as highly important in most computer systems. The goal of this work is to analyze the research published in the proceedings of well-established dependability conferences (i.e., DSN, International Symposium on Software Reliability Engineering (ISSRE), International Symposium on Reliable Distributed Systems (SRDS), European Dependable Computing Conference (EDCC), Latin-American Symposium on Dependable Computing (LADC), Pacific Rim International Symposium on Dependable Computing (PRDC)), while using Natural Language Processing (NLP) and namely the Latent Dirichlet Allocation (LDA) algorithm to identify active, collapsing, ephemeral, and new lines of research in the dependability field. Results show a strong emphasis on terms, like ‘security’, despite the general focus of the conferences in dependability and new trends that are related with ’machine learning’ and ‘blockchain’. We used the PRDC conference as a use case, which showed similarity with the overall set of conferences, although we also found specific terms, like ‘cyber-physical’, being popular at PRDC and not in the overall dataset.