International Technology Roadmap for Semiconductors 2003の要求清浄度について － シリコンウエハ表面と雰囲気環境に要求される清浄度, 分析方法の現状について －

High-performance CMOS variability in the 65-nm regime and beyond. IBM J Res And Dev

ion is the selection and (possibly) coarsening (i.e. quantisation) of certain system characteristics (attributes, performance indicators, parameters) from the total set of system characteristics. The abstraction process comprises: • A simplification (Example: The colour Red is an abstraction, which neglects the different possible shades of Red, the wavelengths, the intensity etc.), • an aggregation (Example: ‘Temperature’ condenses the myriad of individual molecule movements in a gas volume into a single number.), • and consequently: loss of information. The opposite of abstraction is concretisation. It comprises: • gain of information, • detailing, • refinement, • disaggregation, • in engineering: the design process. 3.2.2 System Boundary Systems are defined by their system boundary, which makes a distinction between inside and outside possible (i.e. between self and non-self, see Sect. 4.1). When 3.2 What is a System? 87 Fig. 3.3 Trade-off between initial development cost and later adaptation cost systems are designed, we must choose where to place the boundary. This involves a consideration of cost (Fig. 3.3): A narrow boundary will reduce the cost for planning and development in the first place but bears the risk of a high effort in case a subsequent adaptation and extension of the system should be necessary. A wide system boundary reverses the cost curves. Apparently, the total cost minimum lies somewhere in the middle. 3.2.3 Some System Types and Properties Most systems are transient, i.e. they change over time. They are developed, assembled, modified, destroyed. Reactive systems react on inputs (events, signals, sensor data) by applying outputs (events, control signals, commands) to the environment. All open systems are reactive! In a more focused definition: Only systems reacting in real time (within a predefined time) are reactive systems. Such real-time systems are characterised by (1) time restrictions (deadlines), and (2) time determinism (i.e. guarantees). Planned vs. Unplanned systems: During the development process of technical systems, only predictable events are taken into account in the designed core. But there will always be an unpredictable rest. Therefore, the ‘spontaneous closure’ (Fig. 3.4) has to cover these events. In technical systems, the exception handler or the diagnosis system play the role of a simple spontaneous closure. Living systems are characterised by a powerful spontaneous closure. Recurring reactions of the spontaneous closure become part of the designed core. This amounts to learning.

Organic Computing – Technical Systems for Survival in the Real World

The convergence and divergence are two common phenomena in swarm intelligence. To obtain good search results, the algorithm should have a balance on convergence and divergence. The premature convergence happens partially due to the solutions getting clustered together, and not diverging again. The brain storm optimization (BSO), which is a young and promising algorithm in swarm intelligence, is based on the collective behavior of human being, that is, the brainstorming process. The convergence strategy is utilized in BSO algorithm to exploit search areas may contain good solutions. The new solutions are generated by divergence strategy to explore new search areas. Premature convergence also happens in the BSO algorithm. The solutions get clustered after a few iterations, which indicate that the population diversity decreases quickly during the search. A definition of population diversity in BSO algorithm is introduced in this paper to measure the change of solutions’ distribution. The algorithm’s exploration and exploitation ability can be measured based on the change of population diversity. Different kinds of partial reinitialization strategies are utilized to improve the population diversity in BSO algorithm. The experimental results show that the performance of the BSO is improved by part of solutions re-initialization strategies.

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Population Diversity Maintenance In Brain Storm Optimization Algorithm

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

We present a methodology for autonomous optimization of multi-processor system-on-chips (MPSoC) using distributed learning classifier systems (LCS). The methodology promises to offer a high design reuse rate and to reduce design complexity. The distributed LCS autonomously optimize each core of an MPSoC and, by exchanging classifiers, help each other in achieving their individual goals, compensating reciprocal heating of the cores. We validate our methodology with a realistic simulation of an MPSoC inspired by the Cell processor, where the LCSs set the frequency and voltage of the cores to maximize each core's performance while minimizing temperature-dependent timing errors. In our example application, selecting emigrants by fitness, communicating along a complete graph, and deleting by prediction error results in the highest performance for each core that allows operation with almost no temperature-dependent timing errors but retains self-adaptation capabilities. The results show how distributed LCS autonomously optimize the cores of an MPSoC, greatly reducing design efforts through a reusable self-adaptation component and an according design methodology.

Autonomous multi-processor-SoC optimization with distributed learning classifier systems XCS

This article presents the use of decentralised self-organisation concepts for the efficient dynamic parameterisation of hardware components and the autonomic distribution of tasks in a symmetrical multi-core processor system. Using results obtained with an autonomic system-on-chip hardware demonstrator, we show that Learning Classifier Tables, a simplified XCS-based reinforcement learning technique optimised for a low-overhead hardware implementation and integration, achieve nearly optimal results for task-level dynamic workload balancing during run time for a standard networking application. Further investigations show the quantitative differences in optimisation quality between scenarios when local and global system information is available to the classifier rules. Autonomic workload management or task repartitioning at run time relieves the software application developers from exploring this NP-hard problem during design time, and is able to react to dynamic and unforeseeable changes in the MPSoC operating environment.

Applying ASoC to Multi-core Applications for Workload Management

This work-in-progress paper surveys error detection techniques for transient, timing, permanent and logical errors in system-on-chip (SoC) design and discusses their applicability in the design of monitors for our Autonomic SoC architecture framework. These monitors will be needed to deliver necessary signals to achieve fault-tolerance, self-healing and self-calibration in our Autonomic SoC architecture. The framework combines the monitors with a well-tailored design methodology that explores how the Autonomic SoC (ASoC) can cope with malfunctioning subcomponents.

/pdf/error-detection-techniques-applicable-in-an-architecture-hn5hb2817a.pdf

Error Detection Techniques Applicable in an Architecture Framework and Design Methodology for Autonomic SoCs

CMOS technology downscaling has enabled the integration of more complex algorithms in System-On-Chips (SoCs). Nevertheless, design complexity and deep-submicron related reliability problems are hindering CMOS evolution. Organic Computing is a new research direction addressing these challenges by embedding life-like principles in SoCs. In this article, we present an autonomic SoC (ASoC) platform. It extends the functional SoC components with autonomic elements in order to build a distributed autonomic SoC. We also present how different autonomic components, including a learning classifier based decision system, adapt to changing environments and globally optimise SoC parameters. A simulation based evaluation is then presented.

Autonomic System on Chip Platform.

CMOS technology downscaling has enabled the integration of more complex algorithms in System-On-Chips (SoCs) Nevertheless, design complexity and deep-submicron related reliability problems are hindering CMOS evolution Organic Computing is a new research direction addressing these challenges by embedding life-like principles in SoCs In this article, we present an autonomic SoC (ASoC) platform It extends the functional SoC components with autonomic elements in order to build a distributed autonomic SoC We also present how different autonomic components, including a learning classifier based decision system, adapt to changing environments and globally optimise SoC parameters A simulation based evaluation is then presented

Andreas Bernauer

Papers

Autonomous multi-processor-SoC optimization with distributed learning classifier systems XCS

Applying ASoC to Multi-core Applications for Workload Management

Error Detection Techniques Applicable in an Architecture Framework and Design Methodology for Autonomic SoCs

Autonomic System on Chip Platform.

Autonomic System on Chip Platform