Cover Image

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

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Prakriya, Neha, Ding, Zijian, Sun, Yizhou, Cong, Jason
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Iceberg: Enhancing HLS Modeling with Synthetic Data
by: Ding, Zijian, et al.
Published: (2025)

Efficient Task Transfer for HLS DSE
by: Ding, Zijian, et al.
Published: (2024)

Optimized Multi-Token Joint Decoding with Auxiliary Model for LLM Inference
by: Qin, Zongyue, et al.
Published: (2024)

HMT: Hierarchical Memory Transformer for Efficient Long Context Language Processing
by: He, Zifan, et al.
Published: (2024)

FlexLLM: Composable HLS Library for Flexible Hybrid LLM Accelerator Design
by: Zhang, Jiahao, et al.
Published: (2026)

LIFT: Last-Mile Fine-Tuning for Table Explicitation
by: Khaitan, Divij, et al.
Published: (2026)

LIFT+: Lightweight Fine-Tuning for Long-Tail Learning
by: Shi, Jiang-Xin, et al.
Published: (2025)

Dynamic-Width Speculative Beam Decoding for Efficient LLM Inference
by: Qin, Zongyue, et al.
Published: (2024)

LIFT the Veil for the Truth: Principal Weights Emerge after Rank Reduction for Reasoning-Focused Supervised Fine-Tuning
by: Liu, Zihang, et al.
Published: (2025)

LLM-DSE: Searching Accelerator Parameters with LLM Agents
by: Wang, Hanyu, et al.
Published: (2025)

A Unified Framework for Automated Code Transformation and Pragma Insertion
by: Pouget, Stéphane, et al.
Published: (2024)

Stabilizing LLM Supervised Fine-Tuning via Explicit Distributional Control
by: Wang, Xinyu, et al.
Published: (2026)

Hierarchical Mixture of Experts: Generalizable Learning for High-Level Synthesis
by: Li, Weikai, et al.
Published: (2024)

Goal-Conditioned Supervised Learning for LLM Fine-Tuning
by: Li, Shijun, et al.
Published: (2026)

LLM Zeroth-Order Fine-Tuning is an Inference Workload
by: Li, Zelin, et al.
Published: (2026)

Cross-Modality Program Representation Learning for Electronic Design Automation with High-Level Synthesis
by: Qin, Zongyue, et al.
Published: (2024)

Proximal Supervised Fine-Tuning
by: Zhu, Wenhong, et al.
Published: (2025)

Supervised Fine-Tuning as Inverse Reinforcement Learning
by: Sun, Hao
Published: (2024)

Closed-Loop Supervised Fine-Tuning of Tokenized Traffic Models
by: Zhang, Zhejun, et al.
Published: (2024)

Robust and Generalizable GNN Fine-Tuning via Uncertainty-aware Adapter Learning
by: Jiang, Bo, et al.
Published: (2025)

Automatic Hardware Pragma Insertion in High-Level Synthesis: A Non-Linear Programming Approach
by: Pouget, Stéphane, et al.
Published: (2024)

Anchored Supervised Fine-Tuning
by: Zhu, He, et al.
Published: (2025)

Pragma-VL: Towards a Pragmatic Arbitration of Safety and Helpfulness in MLLMs
by: Wen, Ming, et al.
Published: (2026)

Learning to Compare Hardware Designs for High-Level Synthesis
by: Bai, Yunsheng, et al.
Published: (2024)

ASFT: Aligned Supervised Fine-Tuning through Absolute Likelihood
by: Wang, Ruoyu, et al.
Published: (2024)

On-Policy RL Meets Off-Policy Experts: Harmonizing Supervised Fine-Tuning and Reinforcement Learning via Dynamic Weighting
by: Zhang, Wenhao, et al.
Published: (2025)

MobiLLM: Enabling LLM Fine-Tuning on the Mobile Device via Server Assisted Side Tuning
by: Li, Liang, et al.
Published: (2025)

MoFO: Momentum-Filtered Optimizer for Mitigating Forgetting in LLM Fine-Tuning
by: Chen, Yupeng, et al.
Published: (2024)

Rotation-Preserving Supervised Fine-Tuning
by: Jin, Hangzhan, et al.
Published: (2026)

OAT-Rephrase: Optimization-Aware Training Data Rephrasing for Zeroth-Order LLM Fine-Tuning
by: Long, Jikai, et al.
Published: (2025)

AutoHLS: Learning to Accelerate Design Space Exploration for HLS Designs
by: Ahmed, Md Rubel, et al.
Published: (2024)

Breaking Memorization Barriers in LLM Code Fine-Tuning via Information Bottleneck for Improved Generalization
by: Wang, Changsheng, et al.
Published: (2025)

Pre-training LLM without Learning Rate Decay Enhances Supervised Fine-Tuning
by: Yano, Kazuki, et al.
Published: (2026)

Preserving Diversity in Supervised Fine-Tuning of Large Language Models
by: Li, Ziniu, et al.
Published: (2024)

Understanding the Performance and Estimating the Cost of LLM Fine-Tuning
by: Xia, Yuchen, et al.
Published: (2024)

UFT: Unifying Supervised and Reinforcement Fine-Tuning
by: Liu, Mingyang, et al.
Published: (2025)

Rank Also Matters: Hierarchical Configuration for Mixture of Adapter Experts in LLM Fine-Tuning
by: Cong, Peizhuang, et al.
Published: (2025)

Diversity in Large Language Models under Supervised Fine-Tuning
by: Klypa, Roman, et al.
Published: (2026)

Fine-Tuning Flow Matching via Maximum Likelihood Estimation of Reconstructions
by: Li, Zhaoyi, et al.
Published: (2025)

MPM-LLM4DSE: Reaching the Pareto Frontier in HLS with Multimodal Learning and LLM-Driven Exploration
by: Xu, Lei, et al.
Published: (2026)