1. [Publications](/publications) 2. BufFormer: A Generative ML Framework for Scalable Buffering # BufFormer: A Generative ML Framework for Scalable Buffering ![Publication image](/sites/default/files/styles/wide/public/default_images/default.jpeg?itok=qUFsuJCP "Publication image") Buffering is a prevalent interconnect optimization technique to help timing closure and is often performed after placement. A common buffering approach is to construct a Steiner tree and then buffers are inserted on the tree based on Ginneken-Lillis style algorithm. Such an approach is difficult to scale with large nets. Our work attempts to solve this problem with a generative machine-learning (ML) approach without Steiner tree construction. Our approach can extract and reuse knowledge from high quality samples and therefore has significantly improved scalability. A generative ML framework, BufFormer, is proposed to construct abstract tree topol-ogy while simultaneously determining buffer sizes & locations. A baseline method, FLUTE-based Steiner tree construction followed by Ginneken-Lillis style buffer insertion, is implemented to generate training samples. After training, BufFormer can produce solutions for unseen nets highly comparable to baseline results with a correlation coefficient 0.977 in terms of buffer area and 0.934 for driver-sink delays. On average, BufFormer-generated tree achieves similar de-lays with slightly larger buffer area. And up to 160X speedup can be achieved for large nets when running on a GPU over the baseline on a single CPU thread. ## Authors [Rongjian Liang](/person/rongjian-liang) Siddhartha Nath (NVIDIA) Anand Rajaram (NVIDIA) Jiang Hu (Texas A&M University) Mark Haoxing Ren (NVIDIA) ## Publication Date Monday, January 16, 2023 ## Published in [28th Asia and South Pacific Design Automation Conference](https://www.aspdac.com/aspdac2023/cfp/#:~:text=ASP%2DDAC%202023%20is%20the,silicon%20chips%20in%20the%20world.) ## Research Area [Circuits and VLSI Design](/research-area/circuits) [Generative AI](/research-area/generative-ai) ## External Links [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10044843](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10044843) ## Copyright This material is posted here with permission of the IEEE. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to .