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Summary and Conclusions -A novel methodology is proposed for designing fault-tolerant real-time multi-processor systems-on-a-chip to achieve optimal productivity. The methodology employs the heterogeneous built-in-self-repair (BISR) based on graceful degradation and yield enhancement techniques as an embedded optimization engine. The technique exploits the flexibility provided in task-level scheduling and algorithm selection steps. A hardware fault model is developed for modern super-scalar processors and multi-processors which enables an efficient treatment of the synthesis and compilation goals. For the first time, heterogeneous BISR is used at the task level. The key idea is to adapt scheduling and algorithm selection to the available nonfaulty resources. If there is a fault in memory, the algorithms that use less memory are selected and the scheduler exploits the other abundant resource, viz, the processors, more vigorously to compensate for the loss of part of memory. Similarly, a fault in a processor is backed up by memory. The synthesis approach minimizes the degradation in performance for single or multiple faults using simulated annealing-based algorithm selection, scheduling, and assignment algorithms. On the large set of examples this adaptive algorithm selection and scheduling technique has achieved important improvement of throughput compared to conventional nonadaptive schemes. The experimental results also indicate that important improvement in productivity can be achieved by using the extra throughput gained from the technique.