# Iceberg: Enhancing HLS Modeling with Synthetic Data

arXiv: https://arxiv.org/abs/2507.09948 · HTML: https://mlsanity.com/paper/2507.09948/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in LLM synthetic data generation, one of 13 directions in Synthetic data and self-improvement for LLMs.
- Builds on 33 of its 43 references that are in the map.
- First posted on arXiv 14 Jul 2025.

## Placed in

- Synthetic data and self-improvement for LLMs / [LLM synthetic data generation](https://mlsanity.com/synth/llm-synthetic-data-generation/)

## Builds on (most cited first, 30 of 33)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second](https://mlsanity.com/paper/2207.01848/) (2022)
- [Neural Processes](https://mlsanity.com/paper/1807.01622/) (2018)
- [VerilogEval: Evaluating Large Language Models for Verilog Code Generation](https://mlsanity.com/paper/2309.07544/) (2023)
- [Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling](https://mlsanity.com/paper/2207.04179/) (2022)
- [GPT4AIGChip: Towards Next-Generation AI Accelerator Design Automation via Large Language Models](https://mlsanity.com/paper/2309.10730/) (2023)
- [OriGen:Enhancing RTL Code Generation with Code-to-Code Augmentation and Self-Reflection](https://mlsanity.com/paper/2407.16237/) (2024)
- [MG-Verilog: Multi-grained Dataset Towards Enhanced LLM-assisted Verilog Generation](https://mlsanity.com/paper/2407.01910/) (2024)
- [Mixture of In-Context Prompters for Tabular PFNs](https://mlsanity.com/paper/2405.16156/) (2024)
- [LICO: Large Language Models for In-Context Molecular Optimization](https://mlsanity.com/paper/2406.18851/) (2024)
- [AutoDSE: Enabling Software Programmers to Design Efficient FPGA Accelerators](https://mlsanity.com/paper/2009.14381/) (2020)
- [HLSPilot: LLM-based High-Level Synthesis](https://mlsanity.com/paper/2408.06810/) (2024)
- [ExPT: Synthetic Pretraining for Few-Shot Experimental Design](https://mlsanity.com/paper/2310.19961/) (2023)
- [ScaleHLS: A New Scalable High-Level Synthesis Framework on Multi-Level Intermediate Representation](https://mlsanity.com/paper/2107.11673/) (2021)
- [Automated C/C++ Program Repair for High-Level Synthesis via Large Language Models](https://mlsanity.com/paper/2407.03889/) (2024)
- [Allo: A Programming Model for Composable Accelerator Design](https://mlsanity.com/paper/2404.04815/) (2024)
- [C2HLSC: Can LLMs Bridge the Software-to-Hardware Design Gap?](https://mlsanity.com/paper/2406.09233/) (2024)
- [PowerGear: Early-Stage Power Estimation in FPGA HLS via Heterogeneous Edge-Centric GNNs](https://mlsanity.com/paper/2201.10114/) (2022)
- [Cross-Modality Program Representation Learning for Electronic Design Automation with High-Level Synthesis](https://mlsanity.com/paper/2406.09606/) (2024)
- [HIDA: A Hierarchical Dataflow Compiler for High-Level Synthesis](https://mlsanity.com/paper/2311.03379/) (2023)
- [Hierarchical Mixture of Experts: Generalizable Learning for High-Level Synthesis](https://mlsanity.com/paper/2410.19225/) (2024)
- [HLSFactory: A Framework Empowering High-Level Synthesis Datasets for Machine Learning and Beyond](https://mlsanity.com/paper/2405.00820/) (2024)
- [LLM-Aided Compilation for Tensor Accelerators](https://mlsanity.com/paper/2408.03408/) (2024)
- [Optimizing High-Level Synthesis Designs with Retrieval-Augmented Large Language Models](https://mlsanity.com/paper/2410.07356/) (2024)
- [Hierarchical Source-to-Post-Route QoR Prediction in High-Level Synthesis with GNNs](https://mlsanity.com/paper/2401.08696/) (2024)
- [A Unified Framework for Automated Code Transformation and Pragma Insertion](https://mlsanity.com/paper/2405.03058/) (2024)
- [Stream-HLS: Towards Automatic Dataflow Acceleration](https://mlsanity.com/paper/2501.09118/) (2025)

## Built on by (most cited first, 1 of 1)

- [SyntheticHLS: Building Diverse Synthetic High-Level Synthesis Datasets using LLMs](https://mlsanity.com/paper/2610.00106/) (2026)

## Abstract (arXiv)

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 of these models through pretraining on synthetic data and introduce Iceberg, a synthetic data augmentation approach that expands both large language model (LLM)-generated programs and weak labels of unseen design configurations. Our weak label generation method is integrated with an in-context model architecture, enabling meta-learning from actual and proximate labels. Iceberg improves the geometric mean modeling accuracy by 86.4% when adapt to six real-world applications with few-shot examples and achieves a 2.47× and a 1.12× better offline DSE performance when adapting to two different test datasets. Our open-sourced code is here: https://github.com/UCLA-VAST/iceberg