Selectively breaking data dependences to improve the utilization of idle cycles in algorithm level re-computing data paths |
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Authors: | Kaijie Wu Karri R |
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Affiliation: | Dept. of Electr. & Comput. Eng., Polytech. Univ., Brooklyn, NY, USA; |
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Abstract: | Although algorithm level re-computing techniques can trade-off the fault detection capability vs. time overhead of a Concurrent Error Detection (CED) scheme, they result in 100% time overhead when the strongest CED capability is achieved. Using the idle cycles in the data path to do the re-computation can reduce this time overhead. However, dependences between operations prevent the re-computation from fully utilizing the idle cycles. Deliberately breaking some of these data dependences can further reduce the time overhead associated with algorithm level re-computing. According to the experimental results the proposed technique, it brings time overhead down to 0-60% while the associated hardware overhead is from 12% to 50% depending on the design size. |
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