Yen-Pin Chen

ECO timing optimization using spare cells

We introduce in this paper a new problem of ECO timing optimization using spare-cell rewiring and present the first work for this problem. Spare-cell rewiring is a popular technique for incremental timing optimization and/or functional change after the placement stage. The spare-cell rewiring problem is very challenging because of its dynamic wiring cost nature for selecting a spare cell, while the existing related problems consider only static wiring cost. For the addressed problem, we present a framework of buffer insertion and gate sizing to handle it. In this framework, we present a dynamic programming algorithm considering the dynamic cost, called dynamic cost programming (DCP), for the ECO timing optimization with spare cells. Without loss of solution optimality, we further present an effective pruning method by selecting spare cells only inside an essential bounding polygon to reduce the solution space. The whole framework is integrated into a commercial design flow. Experimental results based on five industry benchmarks show that our method is very effective and efficient in fixing the timing violations of ECO paths.

ECO Timing Optimization Using Spare Cells and Technology Remapping

We introduce in this paper a new problem of post-mask engineering change order (ECO) timing optimization using spare-cell rewiring and present a two-phase framework for this problem. Spare-cell rewiring is a popular technique for incremental timing optimization and/or functional change after the placement stage. The spare-cell rewiring problem is very challenging because of its dynamic wiring cost nature for selecting a spare cell, while the existing related problems consider only static wiring cost: once a standard cell is placed, its physical location is fixed and so is its wiring cost. For the spare-cell rewiring problem, each rewiring could make some spare cells become ordinary standard cells and some standard cells become new spare cells simultaneously. As a result, the wiring cost becomes dynamic and further complicates the optimization process. For the addressed problem, we present a two-phase framework of 1) buffer insertion and gate sizing followed by 2) technology remapping. For Phase 1, we present a dynamic programming algorithm considering the dynamic cost, called dynamic cost programming, for the ECO timing optimization with spare cells. Without loss of solution optimality, we further present an effective pruning method by selecting spare cells only inside an essential bounding polygon to reduce the solution space. For those ECO timing paths that cannot be fixed during Phase 1, we apply technology remapping on the spare cells to restructure the circuit to fix the timing violations. The whole framework is integrated into a commercial design flow. Experimental results based on five industry benchmarks show that our method is very effective and efficient in fixing the timing violations of ECO paths.

A unified triode/saturation model with an improved continuity in the output conductance suitable for CAD of VLSI circuits using deep sub-0.1 /spl mu/m NMOS devices

This paper reports a unified triode/saturation model with an improved continuity in the output conductance suitable for CAD of VLSI circuits using deep sub-0.1 /spl mu/m NMOS devices. As verified by the experimental data, the model shows an accurate prediction of the output conductance characteristics.

A novel depth estimation algorithm of chest compression for feedback of high-quality cardiopulmonary resuscitation based on a smartwatch

INTRODUCTION High-quality cardiopulmonary resuscitation (CPR) is a key factor affecting cardiac arrest survival. Accurate monitoring and real-time feedback are emphasized to improve CPR quality. The purpose of this study was to develop and validate a novel depth estimation algorithm based on a smartwatch equipped with a built-in accelerometer for feedback instructions during CPR. METHODS For data collection and model building, researchers wore an Android Wear smartwatch and performed chest compression-only CPR on a Resusci Anne QCPR training manikin. We developed an algorithm based on the assumptions that (1) maximal acceleration measured by the smartwatch accelerometer and the chest compression depth (CCD) are positively correlated and (2) the magnitude of acceleration at a specific time point and interval is correlated with its neighboring points. We defined a statistic value M as a function of time and the magnitude of maximal acceleration. We labeled and processed collected data and determined the relationship between M value, compression rate and CCD. We built a model accordingly, and developed a smartwatch app capable of detecting CCD. For validation, researchers wore a smartwatch with the preinstalled app and performed chest compression-only CPR on the manikin at target sessions. We compared the CCD results given by the smartwatch and the reference using the Wilcoxon Signed Rank Test (WSRT), and used Bland-Altman (BA) analysis to assess the agreement between the two methods. RESULTS We analyzed a total of 3978 compressions that covered the target rate of 80-140/min and CCD of 4-7 cm. WSRT showed that there was no significant difference between the two methods (P = 0.084). By BA analysis the mean of differences was 0.003 and the bias between the two methods was not significant (95% CI: -0.079 to 0.085). CONCLUSION Our study indicates that the algorithm developed for estimating CCD based on a smartwatch with a built-in accelerometer is promising. Further studies will be conducted to evaluate its application for CPR training and clinical practice.