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By:Bayan Abdel Jalil Nimer,University of Houston--Clear Lake. School of Science and Computer Engineering
Published on 2013 by

The demand for high performance and computational complexity, driven by the recent advances in technology such as high clock rate, shrinking transistor size and lower power consumption techniques, continue to increase in embedded systems leading to an increased soft error rate. In some applications, such as video, these errors are displayed as wrong colored bits and are not problematic; however, in safety critical applications these errors could be catastrophic when occurring during critical parts of an operation leading to a complete loss of functionality and sometimes jeopardizing human lives [48]. As a result, reliability issues are becoming limiting factors on application scalability and longterm survivability in the harsh conditions in which such systems are operated. As embedded systems already house large datasets, traditional techniques to improve reliability such as task redundancy can be very costly in terms of memory space consumption. Earlier research conducted on re-computation has successfully proved to decrease memory space consumption in embedded systems [28, 30, 31, 41, 50]. Consequently, the idea of utilizing ideal times to re-compute a task triggered the interest that these idle time frames of computational resources could be utilized to iteratively recompute tasks to increase reliability of overall design without incurring any additional execution latency, area or extra memory space. That is, all tasks are originally stored in memory and kept there until the end of their lifetimes and if a certain task fails to produce intended result, the recomputed task is used to produce the result and pass it to the next task that needs it without being stored in memory. To further investigate the role of re-computation on maximizing reliability we conducted experiments on random task graphs extracted using the TGFF tool [15], which generates graphs tailored to the areas of allocation and scheduling research in embedded systems. To gain further evidence on the effectiveness of the approach, its feasibility was evaluated on data intensive embedded benchmarks. The results that were observed in heterogeneous architectures and in HW/SW co-design show the effective role that task recomputation may play in increasing the robustness of embedded systems against transient errors. Therefore, incorporating recomputation during different abstraction levels of the design will expand capabilities to develop highly reliable embedded systems.

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