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

• URL: http://arxiv.org/abs/2504.21187v1
• Date: Tue, 29 Apr 2025 21:42:59 GMT
• Title: LIFT: LLM-Based Pragma Insertion for HLS via GNN Supervised Fine-Tuning
• Authors: Neha Prakriya, Zijian Ding, Yizhou Sun, Jason Cong,
• Abstract summary: LIFT is a large language model (LLM)-based coding assistant for HLS that automatically generates performance-critical pragmas.<n>We fine-tune the LLM by tightly integrating and supervising the training process with a graph neural network (GNN)
• Score: 38.679497621876926
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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