Organic computing systems are systems which have the capability to autonomously (re-)organize and adapt themselves. The benefit of such systems with self-x properties is that they are more dependable, as they can compensate for some failures. They are easier to maintain, because they can automatically configure themselves and are more convenient to use because of automatic adaptation to new situations. While organic computing systems have a lot of desired properties, there still exists only little knowledge on how they can be designed and built.In this paper an approach for specification and construction of a class of organic computing systems is presented, called the (RIA). The core idea is that the behaviour of an organic computing system can be split into productive phases and self-x phases. This allows for a generic description of how ``organic'' aspects can be specified and implemented. The approach will be illustrated by applying it to a design methodology for organic computing systems and further refining it to an explicit case study in the domain of production automation.

A Specification and Construction Paradigm for Organic Computing Systems

Editor’s note: Self-awareness is a desirable feature of emerging computing systems. It helps systems to understand, manage, and report on their own system behavior. This paper presents an overview centered around the paradigm of self-awareness in computing systems. —Partha Pratim Pande, Washington State University

Self-Awareness in Systems on Chip— A Survey

This paper introduces a new hardware-based machine learning building block – called Learning Classifier Table (LCT) – for the run-time reliability, performance and power optimization of future generations of Systems-on-Chip. LCT inherits concepts from the reinforcement learning techniques found in Learning Classifier Systems. Prediction weighted LCT rule evaluation is implemented on a clock cycle scale with low hardware complexity.

Learning Classifier Tables for Autonomic Systems on Chip.

As reconfigurable devices' capacities and the complexity of applications that use them increase, the need for self-reliance of deployed systems becomes increasingly prominent. Organic computing paradigms have been proposed for fault-tolerant systems because they promote behaviors that allow complex digital systems to adapt and survive in demanding environments. In this paper, we develop a sustainable modular adaptive redundancy technique (SMART) composed of a two-layered organic system. The hardware layer is implemented on a Xilinx Virtex-4 Field Programmable Gate Array (FPGA) to provide self-repair using a novel approach called reconfigurable adaptive redundancy system (RARS). The software layer supervises the organic activities on the FPGA and extends the self-healing capabilities through application-independent, intrinsic, and evolutionary repair techniques that leverage the benefits of dynamic partial reconfiguration (PR). SMART was evaluated using a Sobel edge-detection application and was shown to tolerate stressful sequences of injected transient and permanent faults while reducing dynamic power consumption by 30% compared to conventional triple modular redundancy (TMR) techniques, with nominal impact on the fault-tolerance capabilities. Moreover, PR is employed to keep the system on line while under repair and also to reduce repair time. Experiments have shown a 27.48% decrease in repair time when PR is employed compared to the full bitstream configuration case.

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Sustainable Modular Adaptive Redundancy Technique Emphasizing Partial Reconfiguration for Reduced Power Consumption

Service orientation promotes a new way to design and implement large-scale distributed applications across organizational and technical boundaries. However, it does not provide sufficient means to cope with the increasing complexity in service-oriented applications. A promising way out of this dilemma is to enable self-organization in serviceoriented computing - as advocated in current research initiatives (e.g. the Organic Computing project). Self-organization helps to keep system complexity hidden from human system participants. In this paper, we propose a reference architecture to establish controlled self-organization in a service-oriented environment with respect to existing reference architectures for SOC and self-organization.

A reference architecture for self-organizing service-oriented computing

The evolution of CMOS technologies leads to integrated circuits with ever smaller device sizes, lower supply voltage, higher clock frequency and more process variability. Intermittent faults effecting logic and timing are becoming a major challenge for future integrated circuit designs. This paper presents an Organic Computing inspired SoC architecture which applies self-organization and self-calibration concepts to build reliable SoCs with lower overheads and a broader fault coverage than classical fault-tolerance techniques. We demonstrate the feasibility of this approach by example on the processing pipeline of a public-domain RISC CPU core.

J. Zeppenfeld

Papers

Organic Computing at the System on Chip Level