Meet ApplyKite Twin, the application assistant that learns with you

Meet your Twin, it learns with you

Your Twin learns with you

Launch week codes:30% off annual Pro20% off monthly Pro

Ends in00h00m00s

Cong Guo

Duke University

United States

Research Interests

Explore related searches

Professors in United States

Articles (17)

Year:

2026

Year:

2026

Year:

2026

Year:

2026

Year:

2025

Year:

2025

Year:

2025

Year:

2025

Year:

2025

Year:

2025

Year:

2025

Year:

2025

Year:

2024

Year:

2024

Year:

2024

Year:

2023

Year:

2023

Collaborators (5)

Hai Li

Clare Boothe Luce Professor

Duke University

UNITED STATES

View details

Yiran Chen

Associate Professor

Duke University

UNITED STATES

View details

Zhigang Ji

Professor

Shanghai Jiao Tong University

CHINA

View details

Yuhao Zhu

University of Rochester

UNITED STATES

View details

Chen Zhang

Assistant Professor

Shanghai Jiao Tong University

CHINA

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Cong Guo

Research Interests

Explore related searches

Articles (17)

Frame Skipping Architecture for Video-Language Model Acceleration

A Full-Stack Framework for GNN Acceleration via Partition–Compiler–Architecture Co-Design

M 2 XFP: A Metadata-Augmented Microscaling Data Format for Efficient Low-bit Quantization

CAMformer: Binary Associative Memory Is All You Need

Circuits to Systems: Co-Designing Efficient AI Hardware

DSTC: Dual-Side Sparse Tensor Core for DNNs Acceleration on Modern GPU Architectures

JUNO++: Optimizing ANNS and Enabling Efficient Sparse Attention in LLM via Ray Tracing Core

A Sample-Free Compilation Framework for Efficient Dynamic Tensor Computation

Ecco: Improving Memory Bandwidth and Capacity for LLMs via Entropy-Aware Cache Compression

Phi: Leveraging Pattern-based Hierarchical Sparsity for High-Efficiency Spiking Neural Networks

Transitive Array: An Efficient GEMM Accelerator with Result Reuse

A Survey: Collaborative Hardware and Software Design in the Era of Large Language Models

JUNO: Optimizing High-Dimensional Approximate Nearest Neighbour Search with Sparsity-Aware Algorithm and Ray-Tracing Core Mapping

Accelerating Sparse DNNs Based on Tiled GEMM

GMLake: Efficient and Transparent GPU Memory Defragmentation for Large-scale DNN Training with Virtual Memory Stitching

AdaptGear: Accelerating GNN Training via Adaptive Subgraph-Level Kernels on GPUs

OliVe: Accelerating Large Language Models via Hardware-friendly Outlier-Victim Pair Quantization

Collaborators (5)

Hai Li

Yiran Chen

Zhigang Ji

Yuhao Zhu

Chen Zhang

Make your next move.