Bayesian theory based switching probability calculation method of critical timing path for on-chip timing slack monitoring

Accurate in-situ monitoring is urgently required for an adaptive performance control system and post silicon validation. For accurate in-situ monitoring, a direct probing method is presented in which monitors directly measure a path delay from real critical timing paths. However, we may not be able to predict when the timing slack monitors would activate since the activation depends on a design structure and input patterns. If a timing slack monitor is rarely activated by timing critical paths, the observability from this monitor would be low and the monitor possibly can be discarded. For this reason, we propose a novel timing slack monitoring methodology based on switching probability of timing critical paths. Switching probability and correlation on critical timing paths are formulated, and the proposed method finds a list of critical path endpoints for the timing slack monitor insertion under given power and area constraints. Experimental results with ISCAS'89 circuits show that, compared to the method which places monitors for all worst critical paths, 16.67 ∼ 97.2% of timing slack monitors are removed and 32.56 ∼ 96.88% of dynamic power reduction from the monitors is achieved by the proposed method.