Integrated Sensor Electronic Front-Ends with Self-X Capabilities

The ongoing vivid advance in integration technologies is giving leverage both to computing systems as well as to sensors and sensor systems. Both conventional computing systems as well as innovative computing systems, e.g., following bio-inspiration from nervous systems or neural networks, require efficient interfacing to an increasing diversity of sensors under the constraints of metrology. The realization of sufficiently accurate, robust, and flexible analog front-ends (AFE) is decisive for the overall application system and quality and requires substantial design expertise both for cells in System-on-Chip (SoC) or chips in System-in-Package (SiP) realizations. Adding robustness and flexibility to sensory systems, e.g., for Industry 4.0., by self-X or self-* features, e.g., self-monitoring, -trimming, or -healing (AFEX) approaches the capabilities met in living beings and is pursued in our research. This paper summarizes on two chips, denoted as Universal-Sensor-Interface-with-self-X-properties (USIX) based on amplitude representation and reports on recently identified challenges and corresponding advanced solutions, e.g., on circuit assessment as well as observer robustness for classic amplitude-based AFE, and transition activities to spike domain representation spiking-analog-front-ends with self-X properties (SAFEX) based on adaptive spiking electronics as the next evolutionary step in AFE development. Key cells for AFEX and SAFEX have been designed in XFAB xh035 CMOS technology and have been subject to extrinsic optimization and/or adaptation. The submitted chip features 62,921 transistors, a total area of 10.89 mm2 (74% analog, 26% digital), and 66 bytes of the configuration memory. The prepared demonstrator will allow intrinsic optimization and/or adaptation for the developed technology agnostic concepts and chip instances. In future work, confirmed cells will be moved to complete versatile and robust AFEs, which can serve both for conventional as well as innovative computing systems, e.g., spiking neurocomputers, as well as to leading-edge technologies to serve in SOCs.

Taking the example of computer systems engineering for the analysis of biological cell systems

In this paper, we discuss the potential for the use of engineering methods that were originally developed for the design of embedded computer systems, to analyse biological cell systems. For embedded systems as well as for biological cell systems, design is a feature that defines their identity. The assembly of different components in designs of both systems can vary widely. In contrast to the biology domain, the computer engineering domain has the opportunity to quickly evaluate design options and consequences of its systems by methods for computer aided design and in particular design space exploration. We argue that there are enough concrete similarities between the two systems to assume that the engineering methodology from the computer systems domain, and in particular that related to embedded systems, can be applied to the domain of cellular systems. This will help to understand the myriad of different design options cellular systems have. First we compare computer systems with cellular systems. Then, we discuss exactly what features of engineering methods could aid researchers with the analysis of cellular systems, and what benefits could be gained.

Generalized disjunction decomposition for evolvable hardware

Evolvable hardware (EHW) refers to self-reconfiguration hardware design, where the configuration is under the control of an evolutionary algorithm (EA). One of the main difficulties in using EHW to solve real-world problems is scalability, which limits the size of the circuit that may be evolved. This paper outlines a new type of decomposition strategy for EHW, the "generalized disjunction decomposition" (GDD), which allows the evolution of large circuits. The proposed method has been extensively tested, not only with multipliers and parity bit problems traditionally used in the EHW community, but also with logic circuits taken from the Microelectronics Center of North Carolina (MCNC) benchmark library and randomly generated circuits. In order to achieve statistically relevant results, each analyzed logic circuit has been evolved 100 times, and the average of these results is presented and compared with other EHW techniques. This approach is necessary because of the probabilistic nature of EA; the same logic circuit may not be solved in the same way if tested several times. The proposed method has been examined in an extrinsic EHW system using the (1 + lambda) evolution strategy. The results obtained demonstrate that GDD significantly improves the evolution of logic circuits in terms of the number of generations, reduces computational time as it is able to reduce the required time for a single iteration of the EA, and enables the evolution of larger circuits never before evolved. In addition to the proposed method, a short overview of EHW systems together with the most recent applications in electrical circuit design is provided.

A comprehensive overview of the applications of artificial life

We review the applications of artificial life (ALife), the creation of synthetic life on computers to study, simulate, and understand living systems. The definition and features of ALife are shown by application studies. ALife application fields treated include robot control, robot manufacturing, practical robots, computer graphics, natural phenomenon modeling, entertainment, games, music, economics, Internet, information processing, industrial design, simulation software, electronics, security, data mining, and telecommunications. In order to show the status of ALife application research, this review primarily features a survey of about 180 ALife application articles rather than a selected representation of a few articles. Evolutionary computation is the most popular method for designing such applications, but recently swarm intelligence, artificial immune network, and agent-based modeling have also produced results. Applications were initially restricted to the robotics and computer graphics, but presently, many different applications in engineering areas are of interest.