3 citations · 3 across the 12 of their papers we have counts for
12 papers
Optimus: Elastic Decoding for Efficient Diffusion LLM Serving
Chiyue Wei, Cong Guo, Bowen Duan +7
Large language model (LLM) serving is fundamentally limited by inefficient hardware utilization. Autoregressive (AR) decoding underutilizes GPUs due to its strictly sequential exec…
FlashFPS: Efficient Farthest Point Sampling for Large-Scale Point Clouds via Pruning and Caching
Yuzhe Fu, Hancheng Ye, Cong Guo +7
Point-based Neural Networks (PNNs) have become a key approach for point cloud processing. However, a core operation in these models, Farthest Point Sampling (FPS), often introduces…
Enhance Quantum Teleportation with Multi-Axis Measurement
Junyao Zhang, Jonathan Ku, Zhiding Liang +3
Quantum teleportation is a cornerstone of quantum information processing, enabling the nonlocal transmission of quantum states across arbitrary distances using shared entanglement…
Focus: A Streaming Concentration Architecture for Efficient Vision-Language Models
Chiyue Wei, Cong Guo, Junyao Zhang +8
Vision-Language Models (VLMs) have demonstrated strong performance on tasks such as video captioning and visual question answering. However, their growing scale and video-level inp…
Platinum: Path-Adaptable LUT-Based Accelerator Tailored for Low-Bit Weight Matrix Multiplication
Haoxuan Shan, Cong Guo, Chiyue Wei +4
The rapid scaling of large language models demands more efficient hardware. Quantization offers a promising trade-off between efficiency and performance. With ultra-low-bit quantiz…
AutoRAC: Automated Processing-in-Memory Accelerator Design for Recommender Systems
Feng Cheng, Tunhou Zhang, Junyao Zhang +6
The performance bottleneck of deep-learning-based recommender systems resides in their backbone Deep Neural Networks. By integrating Processing-In-Memory~(PIM) architectures, resea…
Ecco: Improving Memory Bandwidth and Capacity for LLMs via Entropy-aware Cache Compression
Feng Cheng, Cong Guo, Chiyue Wei +7
Large language models (LLMs) have demonstrated transformative capabilities across diverse artificial intelligence applications, yet their deployment is hindered by substantial memo…
Improving Routability Prediction via NAS Using a Smooth One-shot Augmented Predictor
Arjun Sridhar, Chen-Chia Chang, Junyao Zhang +1
Routability optimization in modern EDA tools has benefited greatly from using machine learning (ML) models. Constructing and optimizing the performance of ML models continues to be…
qGDP: Quantum Legalization and Detailed Placement for Superconducting Quantum Computers
Junyao Zhang, Guanglei Zhou, Feng Cheng +6
Noisy Intermediate-Scale Quantum (NISQ) computers are currently limited by their qubit numbers, which hampers progress towards fault-tolerant quantum computing. A major challenge i…
A Survey: Collaborative Hardware and Software Design in the Era of Large Language Models
Cong Guo, Feng Cheng, Zhixu Du +21
The rapid development of large language models (LLMs) has significantly transformed the field of artificial intelligence, demonstrating remarkable capabilities in natural language…
ModSRAM: Algorithm-Hardware Co-Design for Large Number Modular Multiplication in SRAM
Jonathan Ku, Junyao Zhang, Haoxuan Shan +6
Elliptic curve cryptography (ECC) is widely used in security applications such as public key cryptography (PKC) and zero-knowledge proofs (ZKP). ECC is composed of modular arithmet…
Qplacer: Frequency-Aware Component Placement for Superconducting Quantum Computers
Junyao Zhang, Hanrui Wang, Qi Ding +7
Noisy Intermediate-Scale Quantum (NISQ) computers face a critical limitation in qubit numbers, hindering their progression towards large-scale and fault-tolerant quantum computing.…