Synthesis of Hardware Performance Monitoring and Prediction Flow Adapting to Near-Threshold Computing and Advanced Process Nodes

An elaborate hardware performance monitor (HPM) has become increasingly important for handling huge performance variation of near-threshold computing and recent process technologies. In this paper, we propose a new approach to the problem of predicting critical path delays (CPDs) using HPM. Precisely, for a target circuit or system, we formulate the problem of finding an efficient combination of ring oscillators (ROs) for accurate prediction of CPDs on the circuit as a mixed integer second-order cone programming and propose a method of minimizing the total number of ROs for a given pessimism level of prediction. Then, we propose a prediction flow of CPDs through statistical estimation of process parameters from measurements of the customized HPM and machine learning based delay mapping from the estimation. For a set of benchmark circuits tested using 28nm PDK and 0.6V operation, it is shown that our approach is very effective, reducing the pessimism of CPDs and minimum supply voltages by 6.7~52.9% and 20.6~50.8% over those of conventional approaches, respectively.