Presentation
CTS with Machine Learning NDR
DescriptionCTS (Clock Tree Synthesis) is important to optimize design. It is necessary of CTS as Design Methodology to get robust clock tree in terms of latency, skew, physical track, clock tree depth and clock power. There are many ways to synthesis and optimize by changing the type of clock cells or by locating the clock cells with fixed NDR rules for BEOL. (NDR is non default rule for clock net routing which defined routing width, spacing and shielding rule). But there were a few studies for NDR.
It is necessary to get optimal NDR with Machine learning in advance from CTS by apply differential NDR for each nets based on Machine Learning.
By using various delay depending on different clock width, CTS can be optimized more. However, Since it is hard to change or choose the different NDR on every clock nets, Machine learning algorithm is necessary to get more better optimal clock tree.
The result of gain is high without any side effects.
It is necessary to get optimal NDR with Machine learning in advance from CTS by apply differential NDR for each nets based on Machine Learning.
By using various delay depending on different clock width, CTS can be optimized more. However, Since it is hard to change or choose the different NDR on every clock nets, Machine learning algorithm is necessary to get more better optimal clock tree.
The result of gain is high without any side effects.
Event Type
Engineering Poster
TimeTuesday, June 245:00pm - 6:00pm PDT
LocationEngineering Posters, Level 2 Exhibit Hall