Fine-Grained Software-Based Self-Repair of VLIW Processors

This paper describes a fine-grained software-based self-repair method for statically scheduled superscalar processors. An important property of this processor type is that for each operation of the executed program it is known in advance, which resources of the processor will be used by that operation. A scheduling algorithm is introduced that employs this knowledge in order to rearrange the operations in a VLIW program in the field in such a way that components with permanent faults are no longer used. It is explained, how the scheduling algorithm bypasses these failure points such that the affected components can be used partially, even if they contain some permanent faults. The fine-grained self-repair approach is compared with state-of-the art coarse-grained approaches. It turns out that the number of systems that are still running after injecting 10 faults is about 80%, while less than 1% of these systems will survive if a coarse-grained approach is used.