23 citations · 34 across the 11 of their papers we have counts for
11 papers
Co-Designing Graph-based Approximate Nearest Neighbor Search at Billion Scale for Processing-in-Memory
Sitian Chen, Yusen Li, Yao Chen +3
Approximate Nearest Neighbor Search (ANNS) is a core primitive in modern AI systems, and graph-based methods currently offer the best accuracy-efficiency trade-off at scale. The wo…
XtraMAC: An Efficient MAC Architecture for Mixed-Precision LLM Inference on FPGA
Feng Yu, Hongshi Tan, Yao Chen +2
The widespread adoption of mixed-precision quantization in large language models (LLMs) has created demand for hardware that can efficiently perform multiply-accumulate (MAC) opera…
HLS-Seek: QoR-Aware Code Generation for High-Level Synthesis via Proxy Comparative Reward Reinforcement Learning
Qingyun Zou, Feng Yu, Hongshi Tan +3
High-Level Synthesis (HLS) compiles algorithmic C/C++ descriptions into hardware, with Quality of Results (QoR)---latency and resource utilization---critically governed by pragma c…
RidgeWalker: Perfectly Pipelined Graph Random Walks on FPGAs
Hongshi Tan, Yao Chen, Xinyu Chen +4
Graph Random Walks (GRWs) offer efficient approximations of key graph properties and have been widely adopted in many applications. However, GRW workloads are notoriously difficult…
Blurred Encoding for Trajectory Representation Learning
Silin Zhou, Yao Chen, Shuo Shang +3
Trajectory representation learning (TRL) maps trajectories to vector embeddings and facilitates tasks such as trajectory classification and similarity search. State-of-the-art (SOT…
HLStrans: Dataset for C-to-HLS Hardware Code Synthesis
Qingyun Zou, Nuo Chen, Yao Chen +2
High-Level Synthesis (HLS) enables hardware design from C/C++ kernels but requires extensive transformations, such as restructuring code, inserting pragmas, adapting data types, an…
Revisiting the Design of In-Memory Dynamic Graph Storage
Jixian Su, Chiyu Hao, Shixuan Sun +7
The effectiveness of in-memory dynamic graph storage (DGS) for supporting concurrent graph read and write queries is crucial for real-time graph analytics and updates. Various meth…
Evaluating Small Language Models for News Summarization: Implications and Factors Influencing Performance
Borui Xu, Yao Chen, Zeyi Wen +2
The increasing demand for efficient summarization tools in resource-constrained environments highlights the need for effective solutions. While large language models (LLMs) deliver…
T-Edge: Trusted Heterogeneous Edge Computing
Jiamin Shen, Yao Chen, Weng-Fai Wong +1
Heterogeneous computing, which incorporates GPUs, NPUs, and FPGAs, is increasingly utilized to improve the efficiency of computer systems. However, this shift has given rise to sig…
HongTu: Scalable Full-Graph GNN Training on Multiple GPUs (via communication-optimized CPU data offloading)
Qiange Wang, Yao Chen, Weng-Fai Wong +1
Full-graph training on graph neural networks (GNN) has emerged as a promising training method for its effectiveness. Full-graph training requires extensive memory and computation r…
LightRW: FPGA Accelerated Graph Dynamic Random Walks
Hongshi Tan, Xinyu Chen, Yao Chen +2
Graph dynamic random walks (GDRWs) have recently emerged as a powerful paradigm for graph analytics and learning applications, including graph embedding and graph neural networks.…