While the most characteristic feature of a smart system is its capability of sensing a set of environmental quantities and actuating appropriate actions in response to those signals, it is obvious that a significant part of its functional operations is involved with the elaboration of the information carried by the signals [14]. This elaboration is usually done after converting the analog, asynchronous environmental signals into the digital domain.Part of the smartness of a smart system is therefore expressed in the autonomous and transparent operation based on closed loop control and predictive capabilities, as well as improved signal processing technologies. The former functions are normally carried out by a micro-controller or processor core, whereas the latter ones rely on either a digital signal processor (DSP) or an application-specific integrated circuit (ASIC). Hybrid architectures, that combine one or more general purpose CPUs with one or more hardware accelerators are also increasingly popular [11].Such "processing" dimension, coupled with the energy-autonomous nature of these systems put significant emphasis on their energy efficiency [4,12]. Measures for reducing energy (and power) consumption vary according to the engineering domain of the component being considered. In the computing subsystem, classical low-power techniques for processors and digital circuits can be fruitfully exploited [35]. In this chapter, however, we focus on the explicit signal processing task and show how we can effectively leverage an emerging design paradigm called approximate computing [20,52].
