6 citations · 10 across the 4 of their papers we have counts for
5 papers
SyntheticHLS: Building Diverse Synthetic High-Level Synthesis Datasets using LLMs
Stefan Abi-Karam, Miaoyan Zhou, Callie Hao
Deep learning and large language models (LLMs) are rapidly gaining adoption in semiconductor design, driving demand for training datasets. Most efforts focus on hardware descriptio…
Benchmarking Agentic HLS Design Tasks With HLS-Eval
Stefan Abi-Karam, Callie Hao
Large language models (LLMs) and AI agents are increasingly explored for hardware design, including high-level digital design. While most work targets code generation and editing f…
HLSFactory-Agent: Large-Scale Agentic HLS Dataset Construction from Academic and Open-Source Projects
Kaushik Chandana, Jay Imperatori, Tanmay Shukla +3
Building large, diverse datasets of high-level synthesis (HLS) designs beyond common community benchmarks remains an open challenge. This challenge is made urgent by the rise of de…
HLS-Eval: A Benchmark and Framework for Evaluating LLMs on High-Level Synthesis Design Tasks
Stefan Abi-Karam, Cong Hao
The rapid scaling of large language model (LLM) training and inference has driven their adoption in semiconductor design across academia and industry. While most prior work evaluat…
HLSFactory: A Framework Empowering High-Level Synthesis Datasets for Machine Learning and Beyond
Stefan Abi-Karam, Rishov Sarkar, Allison Seigler +7
Machine learning (ML) techniques have been applied to high-level synthesis (HLS) flows for quality-of-result (QoR) prediction and design space exploration (DSE). Nevertheless, the…