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.

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.I.

In this paper, we define the problem of spatial mapping. We present reasons why performing spatial mappings at run-time is both necessary and desirable. We propose what is-to our knowledge-the first attempt at a formal description of spatial mappings for the embedded real-time streaming application domain. Thereby, we introduce criteria for a qualitative comparison of these spatial mappings. As an illustration of how our formalization relates to practice, we relate our own spatial mapping algorithm to the formal model.

Abstract-Design-time application mapping is limited to a predefined set of applications and a static platform. Resource management at run-time is required to handle future changes in the application set, and to provide some degree of fault tolerance, due to imperfect production processes and wear of materials. This paper concerns resource allocation at run-time, allowing multiple real-time applications to run simultaneously on a heterogeneous MPSoC. Low-complexity algorithms are required, in order to respond fast enough to unpredictable execution requests. We present a decomposition of this problem into four phases. The allocation of tasks to specific locations in the platform is the main contribution of this work. Experiments on a real platform show the feasibility of this approach, with execution times in tens of milliseconds for a single allocation attempt.

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, applications 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.