An Improved Methodology for Resilient Design Implementation

Abstract

Resilient design techniques are used to (i) ensure correct operation under dynamic variations and to (ii) improve design performance (e.g., timing speculation). However, significant overheads (e.g., 16% and 14% energy penalties due to throughput degradation and additional circuits) are incurred by existing resilient design techniques. For instance, resilient designs require additional circuits to detect and correct timing errors. Further, when there is an error, the additional cycles needed to restore a previous correct state degrade throughput, which diminishes the performance benefit of using resilient designs. In this work, we describe an improved methodology for resilient design implementation to minimize the costs of resilience in terms of power, area, and throughput degradation. Our methodology uses two levers: selective-endpoint optimization (i. e., sensitivity-based margin insertion) and clock skew optimization. We integrate the two optimization techniques in an iterative optimization flow which comprehends toggle rate information and the trade-off between cost of resilience and margin on combinational paths. Since the error-detection network can result in up to 9% additional wirelength cost, we also propose a matching-based algorithm for construction of the error-detection network to minimize this resilience overhead. Further, our implementations comprehend the impacts of signoff corners (in particular, hold constraints, and use of typical vs. slow libraries) and process variation, which are typically omitted in previous studies of resilience trade-offs. Our proposed flow achieves energy reductions of up to 21% and 10% compared to a conventional (with only margin used to attain robustness) design and a brute-force implementation (i.e., a typical resilient design, where resilient endpoints are (greedily) instantiated at timing-critical endpoints), respectively. We show that these benefits increase in the context of an adaptive voltage scaling strategy