AI-EDA

AI-EDA

Clock Tree Estimation

Clock network power digital chips 40% to reach about total consumption of electricity large part accounts. However, in the actual design stage, accurate prediction is possible at a very late stage, after the clock tree synthesis (CTS). Today’s portable electronic devices have power in terms of consuming a lot of the constraints under it because the design early in the stage power analysis precisely that it is important.

In order to overcome this, the laboratory conducted a study to predict the clock network power with high accuracy at the initial stage of design and at the same time propose a clock tree synthesis input parameter for low power.

PROJECT DETAILS

KEYWORDS Clock tree, Power analysis

Related Publications Pre-layout clock tree estimation and optimization using artificial neural network

ECO Power Optimization

Design In the second half of the engineering change order (ECO) process through design to modify more good optimized results obtained may have. As of ECO leakage optimization in step , some cell the more high threshold voltage, or no longer gate length of having cell to swap the leakage power consumption reduction can have. But this process is timing , such as constraint to It takes a long time because it has to be taken into account repeatedly .

This study machine learning techniques using this fast time in effectively performing a method to study there. In particular, Graph convolutional network (GCN) the leverage gate of the feature as well as a circuit in the context of reflection by each gate of the swap results to predict it.

PROJECT DETAILS

KEYWORDS Engineering change order

Related Publications Fast ECO leakage optimization using graph convolutional network

IR drop analysis

IR drop means the voltage drop that occurs to reach the Vdd pin of the standard cell in the metal wires that make up the power grid. If IR drop occur and the voltage supplied to the standard cell is reduced, the performance of the standard cell can be reduced, resulting in timing violations such as setup violations.

Conventional tool for analyzing IR drops computes IR drops by solving a large linear system of equations. But in the case of dynamic IR drops, the larger the circuit, the more time-consuming it becomes. In this work, we aim to study a model that uses machine learning techniques to quickly predict IR drop.

PROJECT DETAILS

KEYWORDS IR drop

Related Publications Dynamic IR drop prediction using image-to-image translation neural network