14 citations · 37 across the 16 of their papers we have counts for
16 papers
ES-VP : Energy-Shaped Dynamic Visual Prompting for Efficient Model Adaptation
Can Jin, Ying Li, Jingchen Sun +5
Visual prompting (VP) has emerged as a parameter-efficient method for adapting pre-trained models to downstream tasks. However, existing approaches encounter a trade-off between fl…
Reasoning over Precedents Alongside Statutes: Case-Augmented Deliberative Alignment for LLM Safety
Can Jin, Rui Wu, Tong Che +10
Ensuring that Large Language Models (LLMs) adhere to safety principles without refusing benign requests remains a significant challenge. While OpenAI introduces deliberative alignm…
GROOT: Graph Edge Re-growth and Partitioning for the Verification of Large Designs in Logic Synthesis
Kiran Thorat, Hongwu Peng, Yuebo Luo +8
Traditional verification methods in chip design are highly time-consuming and computationally demanding, especially for large scale circuits. Graph neural networks (GNNs) have gain…
LLM-VeriPPA: Power, Performance, and Area Optimization aware Verilog Code Generation with Large Language Models
Kiran Thorat, Jiahui Zhao, Yaotian Liu +5
Large Language Models (LLMs) are gaining prominence in various fields, thanks to their ability to generate high- quality content from human instructions. This paper delves into the…
RankFlow: A Multi-Role Collaborative Reranking Workflow Utilizing Large Language Models
Can Jin, Hongwu Peng, Anxiang Zhang +8
In an Information Retrieval (IR) system, reranking plays a critical role by sorting candidate passages according to their relevance to a specific query. This process demands a nuan…
AdaPI: Facilitating DNN Model Adaptivity for Efficient Private Inference in Edge Computing
Tong Zhou, Jiahui Zhao, Yukui Luo +4
Private inference (PI) has emerged as a promising solution to execute computations on encrypted data, safeguarding user privacy and model parameters in edge computing. However, exi…
International Trade Flow Prediction with Bilateral Trade Provisions
Zijie Pan, Stepan Gordeev, Jiahui Zhao +4
This paper presents a novel methodology for predicting international bilateral trade flows, emphasizing the growing importance of Preferential Trade Agreements (PTAs) in the global…
APEER: Automatic Prompt Engineering Enhances Large Language Model Reranking
Can Jin, Hongwu Peng, Shiyu Zhao +7
Large Language Models (LLMs) have significantly enhanced Information Retrieval (IR) across various modules, such as reranking. Despite impressive performance, current zero-shot rel…
Key Information Retrieval to Classify the Unstructured Data Content of Preferential Trade Agreements
Jiahui Zhao, Ziyi Meng, Stepan Gordeev +4
With the rapid proliferation of textual data, predicting long texts has emerged as a significant challenge in the domain of natural language processing. Traditional text prediction…
Advanced Large Language Model (LLM)-Driven Verilog Development: Enhancing Power, Performance, and Area Optimization in Code Synthesis
Kiran Thorat, Jiahui Zhao, Yaotian Liu +5
The increasing use of Advanced Language Models (ALMs) in diverse sectors, particularly due to their impressive capability to generate top-tier content following linguistic instruct…
MaxK-GNN: Extremely Fast GPU Kernel Design for Accelerating Graph Neural Networks Training
Hongwu Peng, Xi Xie, Kaustubh Shivdikar +6
In the acceleration of deep neural network training, the GPU has become the mainstream platform. GPUs face substantial challenges on GNNs, such as workload imbalance and memory acc…
LinGCN: Structural Linearized Graph Convolutional Network for Homomorphically Encrypted Inference
Hongwu Peng, Ran Ran, Yukui Luo +8
The growth of Graph Convolution Network (GCN) model sizes has revolutionized numerous applications, surpassing human performance in areas such as personal healthcare and financial…
Accel-GCN: High-Performance GPU Accelerator Design for Graph Convolution Networks
Xi Xie, Hongwu Peng, Amit Hasan +7
Graph Convolutional Networks (GCNs) are pivotal in extracting latent information from graph data across various domains, yet their acceleration on mainstream GPUs is challenged by…
AutoReP: Automatic ReLU Replacement for Fast Private Network Inference
Hongwu Peng, Shaoyi Huang, Tong Zhou +11
The growth of the Machine-Learning-As-A-Service (MLaaS) market has highlighted clients' data privacy and security issues. Private inference (PI) techniques using cryptographic prim…
PASNet: Polynomial Architecture Search Framework for Two-party Computation-based Secure Neural Network Deployment
Hongwu Peng, Shanglin Zhou, Yukui Luo +9
Two-party computation (2PC) is promising to enable privacy-preserving deep learning (DL). However, the 2PC-based privacy-preserving DL implementation comes with high comparison pro…
RRNet: Towards ReLU-Reduced Neural Network for Two-party Computation Based Private Inference
Hongwu Peng, Shanglin Zhou, Yukui Luo +11
The proliferation of deep learning (DL) has led to the emergence of privacy and security concerns. To address these issues, secure Two-party computation (2PC) has been proposed as…