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arXiv · 2504.21187

LIFT: LLM-Based Pragma Insertion for HLS via GNN Supervised Fine-Tuning

Neha Prakriya·Zijian Ding·Yizhou Sun·Jason Cong
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Abstract

FPGAs are increasingly adopted in datacenter environments for their reconfigurability and energy efficiency. High-Level Synthesis (HLS) tools have eased FPGA programming by raising the abstraction level from RTL to untimed C/C++, yet attaining high performance still demands expert knowledge and iterative manual insertion of optimization pragmas to modify the microarchitecture. To address this challenge, we propose LIFT, a large language model (LLM)-based coding assistant for HLS that automatically generates performance-critical pragmas given a C/C++ design. We fine-tune the LLM by tightly integrating and supervising the training process with a graph neural network (GNN), combining the sequential modeling capabilities of LLMs with the structural and semantic understanding of GNNs necessary for reasoning over code and its control/data dependencies. On average, LIFT produces designs that improve performance by 3.52x and 2.16x than prior state-of the art AutoDSE and HARP respectively, and 66x than GPT-4o.

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Neha Prakriya, Zijian Ding, Yizhou Sun, Jason Cong. 2025-04-29. LIFT: LLM-Based Pragma Insertion for HLS via GNN Supervised Fine-Tuning. https://arxiv.org/abs/2504.21187

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