4 papers
Calibrating LLMs with Semantic-level Reward
Fengfei Yu, Ruijia Niu, Dongxia Wu +2
As large language models (LLMs) are deployed in consequential settings such as medical question answering and legal reasoning, the ability to estimate when their outputs are likely…
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…