Editorial

Abstract

Nowadays, smart systems are becoming more relevant in a large number of critical sectors, like energy management in public spaces, healthcare, automotive, safety and security. Compared to classical embedded systems, a distinctive aspect for these systems is their smartness, which is the ability to learn from the previous experience and to seemingly react to the surrounding environment. However, this tight interaction with the physical environment implies a high level of heterogeneity in the hardware architecture. At the same time, application scenarios are becoming more complex, since an increasing amount of computation is constrained by tight performance, cost and safety requirements. Novel and comprehensive methodologies are thus required to ease the development of next-generation smart systems, with the goal of reducing design costs and time-to-market, improving the smartness of the architectures, and analyze the dynamic behavior of the resulting systems and their reactivity to unpredictable events. This special issue, entitled Innovative Design Methods for Smart Embedded Systems, tackles such challenges by providing both machine-learning techniques and application-specific optimization solutions that guarantee that the application of smart innovations meets the imposed requirements and constraints.