9 citations · 14 across the 10 of their papers we have counts for
6 papers · 1 filter
Is Agentic AI Ready for Real-World Hardware Engineering? A Deep Dive with Phoenix-bench
Qingyun Zou, Feng Yu, Hongshi Tan +2
We ask whether agentic AI systems built for software engineering transfer to realistic hardware engineering. Existing hardware LLM benchmarks isolate sub-tasks but none jointly req…
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…
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…
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.…
ReGraph: Scaling Graph Processing on HBM-enabled FPGAs with Heterogeneous Pipelines
Xinyu Chen, Yao Chen, Feng Cheng +3
The use of FPGAs for efficient graph processing has attracted significant interest. Recent memory subsystem upgrades including the introduction of HBM in FPGAs promise to further a…
Skew-Oblivious Data Routing for Data-Intensive Applications on FPGAs with HLS
Xinyu Chen, Hongshi Tan, Yao Chen +3
FPGAs have become emerging computing infrastructures for accelerating applications in datacenters. Meanwhile, high-level synthesis (HLS) tools have been proposed to ease the progra…