5 papers
Hardware-accelerated Aggregation: Unification and Specialization
Alireza Shateri, Hongshi Tan, Michael Ng +2
The high efficiency of domain-specific hardware has sparked substantial interest in adopting accelerators in data analytics systems. Among many choices, GPUs and FPGAs thrived as t…
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…
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…
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…