Search Results for author: Jiang Hu

Found 12 papers, 2 papers with code

Decentralized Riemannian natural gradient methods with Kronecker-product approximations

no code implementations16 Mar 2023 Jiang Hu, Kangkang Deng, Na Li, Quanzheng Li

With a computationally efficient approximation of the second-order information, natural gradient methods have been successful in solving large-scale structured optimization problems.

Riemannian Natural Gradient Methods

no code implementations15 Jul 2022 Jiang Hu, Ruicheng Ao, Anthony Man-Cho So, MingHan Yang, Zaiwen Wen

Moreover, we show that if the loss function satisfies certain convexity and smoothness conditions and the input-output map satisfies a Riemannian Jacobian stability condition, then our proposed method enjoys a local linear -- or, under the Lipschitz continuity of the Riemannian Jacobian of the input-output map, even quadratic -- rate of convergence.

Towards Collaborative Intelligence: Routability Estimation based on Decentralized Private Data

no code implementations30 Mar 2022 Jingyu Pan, Chen-Chia Chang, Zhiyao Xie, Ang Li, Minxue Tang, Tunhou Zhang, Jiang Hu, Yiran Chen

To further strengthen the results, we co-design a customized ML model FLNet and its personalization under the decentralized training scenario.

Federated Learning

Toward Taming the Overhead Monster for Data-Flow Integrity

no code implementations19 Feb 2021 Lang Feng, Jiayi Huang, Jeff Huang, Jiang Hu

Data-Flow Integrity (DFI) is a well-known approach to effectively detecting a wide range of software attacks.

Hardware Architecture

Automatic Routability Predictor Development Using Neural Architecture Search

no code implementations3 Dec 2020 Chen-Chia Chang, Jingyu Pan, Tunhou Zhang, Zhiyao Xie, Jiang Hu, Weiyi Qi, Chun-Wei Lin, Rongjian Liang, Joydeep Mitra, Elias Fallon, Yiran Chen

The rise of machine learning technology inspires a boom of its applications in electronic design automation (EDA) and helps improve the degree of automation in chip designs.

BIG-bench Machine Learning Neural Architecture Search

Net2: A Graph Attention Network Method Customized for Pre-Placement Net Length Estimation

no code implementations27 Nov 2020 Zhiyao Xie, Rongjian Liang, Xiaoqing Xu, Jiang Hu, Yixiao Duan, Yiran Chen

Net length is a key proxy metric for optimizing timing and power across various stages of a standard digital design flow.

Graph Attention

Fast IR Drop Estimation with Machine Learning

no code implementations26 Nov 2020 Zhiyao Xie, Hai Li, Xiaoqing Xu, Jiang Hu, Yiran Chen

IR drop constraint is a fundamental requirement enforced in almost all chip designs.

BIG-bench Machine Learning

FIST: A Feature-Importance Sampling and Tree-Based Method for Automatic Design Flow Parameter Tuning

no code implementations26 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.

BIG-bench Machine Learning Feature Importance

Automatic Microprocessor Performance Bug Detection

no code implementations17 Nov 2020 Erick Carvajal Barboza, Sara Jacob, Mahesh Ketkar, Michael Kishinevsky, Paul Gratz, Jiang Hu

Design bugs that affect processor performance rather than its functionality are especially difficult to catch, particularly in new microarchitectures.


Structured Quasi-Newton Methods for Optimization with Orthogonality Constraints

1 code implementation3 Sep 2018 Jiang Hu, Bo Jiang, Lin Lin, Zaiwen Wen, Yaxiang Yuan

In particular, we are interested in applications that the Euclidean Hessian itself consists of a computational cheap part and a significantly expensive part.

Optimization and Control

Adaptive Regularized Newton Method for Riemannian Optimization

2 code implementations7 Aug 2017 Jiang Hu, Andre Milzarek, Zaiwen Wen, Yaxiang Yuan

Optimization on Riemannian manifolds widely arises in eigenvalue computation, density functional theory, Bose-Einstein condensates, low rank nearest correlation, image registration, and signal processing, etc.

Optimization and Control

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