Hardware Performance Monitoring Methodology at Near-Threshold Computing and Advanced Technology Nodes: From Design to Postsilicon

Near-threshold computing is essential for energy-efficient operation of VLSI systems, but wide performance variation and nonlinearity to process variations block the proliferation. To cope with this, in this article, we propose a holistic hardware performance monitoring methodology for accurate timing prediction in a near-threshold voltage regime. Precisely, 1) we formulate the problem of finding an efficient configuration of monitoring circuits into an instance of optimal experiment design problem and 2) propose a new timing prediction flow, consisting of statistical estimation of FEOL and BEOL process variations and a neural network-based timing inference model. For accurate control of timing margin and overcoming simulation-silicon discrepancies, 3) we introduce uncertainty learning in the prediction model construction and calibrate it through transfer learning. Furthermore, 4) we avoid time-consuming SPICE simulations in our methodology by employing efficient but accurate surrogate models. Through simulations using a 28-nm industry PDK and DK characterized at 0.6-V operation, it is shown that our methodology is highly effective, reducing the average prediction pessimism of maximum delay by 77.9% over conventional signoff results while respecting target prediction yield. Besides, for test chips fabricated using a 10-nm process, we demonstrated that our holistic approach from design to postsilicon phase in conjunction with adaptive voltage scaling reduces dynamic power consumption by 28.2%–28.8% on average, in comparison with typical supply voltage operation.