Parallelizable Bayesian optimization for analog and mixed-signal rare failure detection with high coverage.
ICCAD-IEEE ACM International Conference on Computer-Aided Design(2018)
摘要
Due to inherent complex behaviors id stringent requirements in analog and mixed-signal (AMS) systems, verification becomes a key bottleneck in the product development cycle. For the first time, we present a Bayesian optimization (BO) based approach to the challenging problem of verifying AMS circuits with stringent low failure requirements. At the heart of the proposed BO process is a delicate balancing between two competing needs: exploitation of the current statistical model for quick identification of highly likely failures and exploration of undiscovered design space so as to detect hard-to-find failures within a large parametric space. To do so, we simultaneously leverage multiple optimized acquisition functions to explore varying degrees of balancing between exploitation and exploration. This makes it possible to not only detect rare failures which other techniques fail to identify, but also do so with significantly improved efficiency. We further build in a mechanism into the BO process to enable detection of multiple failure regions, hence providing a higher degree of coverage. Moreover, the proposed approach is readily parallelizable, further speeding up failure detection, particularly for large circuits for which acquisition of simulation/measurement data is very time-consuming. Our experimental study demonstrates that the proposed approach is very effective in finding very rare failures and multiple failure regions which existing statistical sampling techniques and other BO techniques can miss, thereby providing a more robust and cost-effective methodology for rare failure detection.
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关键词
stringent requirements,product development cycle,BO process,delicate balancing,quick identification,undiscovered design space,multiple failure regions,statistical sampling techniques,BO techniques,parallelizable Bayesian optimization,mixed-signal rare failure detection,complex behaviors,AMS circuits,statistical model,hard-to-find failures
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