8 papers
AgRefactor: Self-Evolving Agentic Workflow for HLS Compatibility and Performance
Yang Zou, Zijian Ding, Yizhou Sun +1
High-Level Synthesis (HLS) provides a fast path from concepts to silicon, but converting real-world software into synthesizable HLS code remains challenging due to restrictive lang…
LLM-DSE: Searching Accelerator Parameters with LLM Agents
Hanyu Wang, Xinrui Wu, Zijian Ding +6
Even though high-level synthesis (HLS) tools mitigate the challenges of programming domain-specific accelerators (DSAs) by raising the abstraction level, optimizing hardware direct…
Iceberg: Enhancing HLS Modeling with Synthetic Data
Zijian Ding, Tung Nguyen, Weikai Li +3
Deep learning-based prediction models for High-Level Synthesis (HLS) of hardware designs often struggle to generalize. In this paper, we study how to close the generalizability gap…
Learning to Compare Hardware Designs for High-Level Synthesis
Yunsheng Bai, Atefeh Sohrabizadeh, Zijian Ding +6
High-level synthesis (HLS) is an automated design process that transforms high-level code into hardware designs, enabling the rapid development of hardware accelerators. HLS relies…
LIFT: LLM-Based Pragma Insertion for HLS via GNN Supervised Fine-Tuning
Neha Prakriya, Zijian Ding, Yizhou Sun +1
FPGAs are increasingly adopted in datacenter environments for their reconfigurability and energy efficiency. High-Level Synthesis (HLS) tools have eased FPGA programming by raising…
Hierarchical Mixture of Experts: Generalizable Learning for High-Level Synthesis
Weikai Li, Ding Wang, Zijian Ding +4
High-level synthesis (HLS) is a widely used tool in designing Field Programmable Gate Array (FPGA). HLS enables FPGA design with software programming languages by compiling the sou…