no code implementations • ECCV 2020 • Zixuan Jiang, Keren Zhu, Mingjie Liu, Jiaqi Gu, David Z. Pan
In this work, we formulate the decision problem for reversible operators with training time as the objective function and memory usage as the constraint.
no code implementations • 12 Apr 2024 • Amit Sharma, Teodor-Dumitru Ene, Kishor Kunal, Mingjie Liu, Zafar Hasan, Haoxing Ren
This paper presents a comparative analysis of total cost of ownership (TCO) and performance between domain-adapted large language models (LLM) and state-of-the-art (SoTA) LLMs , with a particular emphasis on tasks related to coding assistance 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.
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 • 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.
1 code implementation • 30 Jul 2022 • Zixuan Jiang, Jiaqi Gu, Mingjie Liu, David Z. Pan
In this work, we delve into the gradient matching method from a comprehensive perspective and answer the critical questions of what, how, and where to match.
no code implementations • 13 Jul 2022 • Wei Shi, Hanrui Wang, Jiaqi Gu, Mingjie Liu, David Pan, Song Han, Nan Sun
To address the challenge, we present RobustAnalog, a robust circuit design framework that involves the variation information in the optimization process.
no code implementations • 15 Dec 2021 • Hanqing Zhu, Jiaqi Gu, Chenghao Feng, Mingjie Liu, Zixuan Jiang, Ray T. Chen, David Z. Pan
With the recent advances in optical phase change material (PCM), photonic in-memory neurocomputing has demonstrated its superiority in optical neural network (ONN) designs with near-zero static power consumption, time-of-light latency, and compact footprint.
no code implementations • 29 Sep 2021 • Wei Shi, Hanrui Wang, Jiaqi Gu, Mingjie Liu, David Z. Pan, Song Han, Nan Sun
Specifically, circuit optimizations under different variations are considered as a set of tasks.
1 code implementation • 25 Aug 2021 • Jiaqi Gu, Hanqing Zhu, Chenghao Feng, Mingjie Liu, Zixuan Jiang, Ray T. Chen, David Z. Pan
Deep neural networks (DNN) have shown superior performance in a variety of tasks.
no code implementations • 1 Apr 2021 • Zixuan Jiang, Jiaqi Gu, Mingjie Liu, Keren Zhu, David Z. Pan
Machine learning frameworks adopt iterative optimizers to train neural networks.
1 code implementation • IEEE Design, Automation & Test in Europe Conference & Exhibition (DATE) 2021 • Jiaqi Gu, Chenghao Feng, Zheng Zhao, Zhoufeng Ying, Mingjie Liu, Ray T. Chen, David Z. Pan
Optical neural networks (ONNs) have demonstrated promising potentials for next-generation artificial intelligence acceleration with ultra-low latency, high bandwidth, and low energy consumption.
1 code implementation • journal 2019 • Cheng-Bin Jin, Hakil Kim, Mingjie Liu, In Ho Han, Jae Il Lee, Jung Hwan Lee, Seongsu Joo, Eunsik Park, Young Saem Ahn, Xuenan Cui
In this paper, we propose a method for estimating lumbar spine MR images based on CT images using a novel objective function and a dual cycle-consistent adversarial network (DC2Anet) with semi-supervised learning.