On-Line Dependability Enhancement of Multiprocessor SoCs by Resource Management

This paper describes a new approach towards dependable design of homogeneous multi-processor SoCs in an example satellite-navigation application. First, the NoC dependability is functionally verified via embedded software. Then the Xentium processor tiles are periodically verified via on-line self-testing techniques, by using a new IIP Dependability Manager. Based on the Dependability Manager results, faulty tiles are electronically excluded and replaced by fault-free spare tiles via on-line resource management. This integrated approach enables fast electronic fault detection/diagnosis and repair, and hence a high system availability. The dependability application runs in parallel with the actual application, resulting in a very dependable system. All parts have been verified by simulation

Run-time mapping: dynamic resource allocation in embedded systems

Many desired features of computing platforms, such as increased fault tolerance, variable quality of service, and improved energy efficiency, can be achieved by postponing resource management decisions from design-time to run-time. While multiprocessing has been widespread in embedded systems for quite some time, allocation of (shared) resources is typically done at design-time to meet the constraints of applications. The inherent flexibility of large-scale embedded systems is then reduced to a fixed, static resource allocation derived at design-time. At run-time, unanticipated situations in either the system itself or in its environment may render resources inaccessible that were assumed to be available at design-time. The increased flexibility obtained by run-time resource allocation can be exploited to increase the degree of fault tolerance, quality of service, energy efficiency and to support a higher variability in use-cases. The term run-time mapping is used to refer to resource allocation at run-time to meet the dynamic requirements of applications. The introduction of full-fledged run-time mapping systems in the domain of embedded systems has long been delayed due to the inherent complexity of the problems to be solved. While similar mapping problems have been solved at design-time for a long time already, different analysis and problem solving techniques are required at run-time. The guided local search technique presented in this thesis provides a balance between robustness and overhead. The results of guided local search and the required computation time on synthetic datasets are competitive with industry standard solvers, while the memory footprint is one or two orders of magnitude lower. Therefore, the algorithm can be implemented on an embedded platform. The computation time required for solving the resource allocation problems at run-time may be further reduced by a hybrid form between design-time allocation and run-time adaptation

Adaptive resource allocation for streaming applications

Streaming applications often have latency and throughput requirements due to timing critical signal processing, or the time critical interaction with their environment. Mapping such applications to a multi-core architecture is commonly done at design-time to be able to analyze the complex design-space. However, such design-flows cannot deal with a dynamic platform or a dynamic set of applications. Hardware faults and resources claimed by other applications may render the assumed available resources inaccessible. To avoid the assumptions posed on the state of the platform by a fixed resource allocation, applica- tions should be designed with location transparency in mind. Applications must be analyzed at design-time to determine the required resource budget, independent of which specific resources will be allocated. Sufficient performance can be guaranteed when such applications are mapped onto an architecture in which each resource is arbitrated using a budget scheduler. Within the Cutting edge Reconfigurable ICs for Stream Processing (CRISP) project, a many-core platform is developed that adheres to these requirements. Using the configuration features of the platform, the system is able to control at run-time what resources are being used by the applications. This paper shows that run-time resource allocation can effectively adapt to the available set of resources, providing partial distribution transparency to the user. As an example, a GNSS receiver is mapped to the platform containing faulty hardware components. A few resources remain critical, but in most cases the faulty components can be circumvented, such that adequate resources can be allocated to the application at run-time

CRISP: Cutting Edge Reconfigurable ICs for Stream Processing

The Cutting edge Reconfigurable ICs for Stream Processing (CRISP) project aims to create a highly scalable and dependable reconfigurable system concept for a wide range of tomorrow’s streaming DSP applications. Within CRISP, a network-on-chip based many-core stream processor with dependability infrastructure and run-time resource management is devised, implemented, and manufactured to demonstrate a coarse-grained core-level reconfigurable system with scalable computing power, flexibility, and dependability. This chapter introduces CRISP, presents the concepts, and outlines the preliminary results of a running project

Geometric representation of association between categories

Categorical data, simplex, triangular plot, paired comparisons, rank orders, permutation polytope, center of gravity, BTL model, Rasch model, inertia, association model, variation, multidimensional unfolding, biplot, multinomial response model, loglinear model, forced classification, classification tree,