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.
1 code implementation • 14 Sep 2023 • Mingjie Liu, Nathaniel Pinckney, Brucek Khailany, Haoxing Ren
The increasing popularity of large language models (LLMs) has paved the way for their application in diverse domains.
no code implementations • 30 Nov 2022 • Jiaqi Gu, Ben Keller, Jean Kossaifi, Anima Anandkumar, Brucek Khailany, David Z. Pan
Transformers have attained superior performance in natural language processing and computer vision.
no code implementations • 27 Oct 2022 • Mingjie Liu, HaoYu Yang, Zongyi Li, Kumara Sastry, Saumyadip Mukhopadhyay, Selim Dogru, Anima Anandkumar, David Z. Pan, Brucek Khailany, Haoxing Ren
These synthetic mask images will augment the original limited training dataset used to finetune the lithography model for improved performance.
no code implementations • 8 Jul 2022 • HaoYu Yang, Zongyi Li, Kumara Sastry, Saumyadip Mukhopadhyay, Anima Anandkumar, Brucek Khailany, Vivek Singh, Haoxing Ren
Machine learning techniques have been extensively studied for mask optimization problems, aiming at better mask printability, shorter turnaround time, better mask manufacturability, and so on.
no code implementations • 13 Jun 2022 • Charbel Sakr, Steve Dai, Rangharajan Venkatesan, Brian Zimmer, William J. Dally, Brucek Khailany
Data clipping is crucial in reducing noise in quantization operations and improving the achievable accuracy of quantization-aware training (QAT).
no code implementations • 12 Mar 2022 • HaoYu Yang, Zongyi Li, Kumara Sastry, Saumyadip Mukhopadhyay, Mark Kilgard, Anima Anandkumar, Brucek Khailany, Vivek Singh, Haoxing Ren
Lithography simulation is a critical step in VLSI design and optimization for manufacturability.
no code implementations • 11 Mar 2022 • Yanqing Zhang, Haoxing Ren, Akshay Sridharan, Brucek Khailany
In this paper, we present GATSPI, a novel GPU accelerated logic gate simulator that enables ultra-fast power estimation for industry sized ASIC designs with millions of gates.
no code implementations • 9 Jul 2021 • Haoxing Ren, Matthew Fojtik, Brucek Khailany
High quality standard cell layout automation in advanced technology nodes is still challenging in the industry today because of complex design rules.
no code implementations • 26 Jun 2021 • Jiawei Zhao, Steve Dai, Rangharajan Venkatesan, Brian Zimmer, Mustafa Ali, Ming-Yu Liu, Brucek Khailany, Bill Dally, Anima Anandkumar
Representing deep neural networks (DNNs) in low-precision is a promising approach to enable efficient acceleration and memory reduction.
no code implementations • 8 Feb 2021 • Steve Dai, Rangharajan Venkatesan, Haoxing Ren, Brian Zimmer, William J. Dally, Brucek Khailany
4-bit weights and 8-bit activations achieve near-full-precision accuracy for both BERT-base and BERT-large on SQuAD while reducing area by 26% compared to an 8-bit baseline.
no code implementations • 26 Nov 2020 • Zhiyao Xie, Haoxing Ren, Brucek Khailany, Ye Sheng, Santosh Santosh, Jiang Hu, Yiran Chen
Moreover, the proposed CNN model is general and transferable to different designs.
no code implementations • 26 Nov 2020 • Zhiyao Xie, Guan-Qi Fang, Yu-Hung Huang, Haoxing Ren, Yanqing Zhang, Brucek Khailany, Shao-Yun Fang, Jiang Hu, Yiran Chen, Erick Carvajal Barboza
Experimental results on benchmark circuits show that our approach achieves 25% improvement in design quality or 37% reduction in sampling cost compared to random forest method, which is the kernel of a highly cited previous work.
1 code implementation • Design Automation Conference (DAC) 2019 • Angad S. Rekhi, Brian Zimmer, Nikola Nedovic, Ningxi Liu, Rangharajan Venkatesan, Miaorong Wang, Brucek Khailany, William J. Dally, C. Thomas Gray
We also introduce an energy model to predict the requirements of high-accuracy AMS hardware running large networks and use it to show that for ADC-dominated designs, there is a direct tradeoff between energy efficiency and network accuracy.
no code implementations • 23 May 2017 • Angshuman Parashar, Minsoo Rhu, Anurag Mukkara, Antonio Puglielli, Rangharajan Venkatesan, Brucek Khailany, Joel Emer, Stephen W. Keckler, William J. Dally
Convolutional Neural Networks (CNNs) have emerged as a fundamental technology for machine learning.