On Self-Aware Mixed-Signal Systems Based on S-Δ ADC

On Self-Aware Mixed-Signal Systems Based on S-Δ ADC

Drago Strle (University of Ljubljana, Slovenia) and Janez Trontelj (University of Ljubljana, Slovenia)
DOI: 10.4018/jertcs.2012040105
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In this paper the authors discuss the issues related to the self-awareness of high-resolution, mixed-signal circuits and systems, based on S-? ADC, which is the most important and sensitive module and the key element for analogue to digital conversion. The basic methodology and framework for improving the self-awareness of such systems are presented. The methodology is based on efficient real-time measurements of a high-resolution, mixed-signal system using pseudo random signal source, real-time calculation of a distance between responses, the possibility to adapt measured circuit to minimize the distance, and changing the parameters of a reference system according to learning rules. The use of pseudo-random noise as a signal source leads to efficient and cost-effective measurements that run in parallel to the main signal processing. The calculation of the distance between the system and its reference are theoretically analysed and verified using Matlab model. The response of a system together with the response of high precision analogue to digital converter (ADC) is compared to the response of a bit-true model of a reference digital circuit. The differences are calculated using simple area-efficient cross-correlation algorithm. Together with adaptation strategy and tuning circuitry it forms the basis for self-awareness of mixed-signal circuits.
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Self-awareness in humans and animals is described and defined in many different ways and from many different perspectives in many different texts, for example: philosophy (Güzeldere, 1995), modern sciences (Cohen, 2002), cognitive sciences (Baars, 1988), and psychology (Rochat, 2003). An insightful, modern overview is presented in Tonnini (2008). Self-awareness theory states that “when we focus our attention on ourselves, we evaluate and compare our current behaviour to our internal standards and values” (Amir, 2004). For man-made systems, the definition of self-awareness remains almost identical despite the fact that such systems are much simpler. For computer systems assisted by “artificial intelligence” the self-awareness is defined in three different ways (Amir, 2004):

  • Explicit self-awareness: where a computer system has a self-model that represents knowledge about itself, the current situation, activities, goals, knowledge, etc., in a form that lends itself to use by a general reasoning system.

  • Self-monitoring: the computer system monitors its internal processes and acts according to some rules.

  • Self-explanation: the agent can recount and justify its actions and inferences.

Here, we will work with the second definition, the self-monitoring, specifically with self-monitoring of a mixed-signal VLSI circuits based on Σ−Δ analogue to digital converter that include some analogue and some digital functionality. In addition, the ADC may be the only mixed-signal module in a system, or just a part that performs the analogue-to-digital conversion of the analogue signals coming from other analogue or mixed-signal modules. The corresponding digital signal processing (DSP) executes part of the hardware or software algorithm that may help to evaluate the results of the monitoring process and act according to some built-in rules.

Monitoring is an essential part of any self-aware and/or self-adaptive system (Amir, 2004); it is a new and difficult topic for digital systems. Several different ideas in the context of self-awareness have recently appeared. The idea presented by Santambrogio (2010) is to monitor and adapt the behaviour of a distributed computer system and to find the best way to accomplish a given goal, despite changing environmental conditions and demands. The work is based on the properties that a self-aware computing system should be goal-oriented, adaptive, have self-healing properties, and be approximate in the sense that it uses the least amount of computational resources to meet the goals. To achieve those properties for computer digital systems, the authors use “Application Heartbeats,” which provide a standardised way for applications to report their performances to an external observer. Many other techniques and ideas exist that are related to the self-awareness of digital systems. For example, the autonomic computing paradigm (Kephart, 2003) deals with a network of processors, and performs the necessary operation without the need of dedicated attention, with the main objectives of self-configuration, self-healing, self-optimization, self-monitoring, and self-awareness. Another idea is known as “organic” computing (Gudeman, 2008) and “bio-inspired embedded system” (Madrenas, 2011). All of them are related to computer hardware and/or software systems.

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