ChipNeMo: Domain-Adapted LLMs for Chip Design

no code implementations • 31 Oct 2023 • Mingjie Liu, Teodor-Dumitru Ene, Robert Kirby, Chris Cheng, Nathaniel Pinckney, Rongjian Liang, Jonah Alben, Himyanshu Anand, Sanmitra Banerjee, Ismet Bayraktaroglu, Bonita Bhaskaran, Bryan Catanzaro, Arjun Chaudhuri, Sharon Clay, Bill Dally, Laura Dang, Parikshit Deshpande, Siddhanth Dhodhi, Sameer Halepete, Eric Hill, Jiashang Hu, Sumit Jain, Ankit Jindal, Brucek Khailany, George Kokai, Kishor Kunal, Xiaowei Li, Charley Lind, Hao liu, Stuart Oberman, Sujeet Omar, Ghasem Pasandi, Sreedhar Pratty, Jonathan Raiman, Ambar Sarkar, Zhengjiang Shao, Hanfei Sun, Pratik P Suthar, Varun Tej, Walker Turner, Kaizhe Xu, Haoxing Ren

ChipNeMo aims to explore the applications of large language models (LLMs) for industrial chip design.

Chatbot Domain Adaptation +2

Mixture Distributions for Scalable Bayesian Inference

no code implementations • 25 Sep 2019 • Pranav Poduval, Hrushikesh Loya, Rajat Patel, Sumit Jain

We show Ensembles of deterministic and stochastic Neural Networks can indeed be cast as an approximate Bayesian inference.

Bayesian Inference