mlsanityData to 14 Oct 2026

Advancing AI-assisted Hardware Design with Hierarchical Decentralized Training and Personalized Inference-Time Optimization

arXiv 2506.00002 ↗First posted 21 Apr 2025cs.AR cs.AI cs.DCcited by 0

Not on the map. Why?

Abstract (arXiv)

Recent years have witnessed a significant increase in the adoption of AI techniques to enhance electronic design automation. In particular, the emergence of Large Language Models (LLMs) has sparked significant interest in LLM-assisted hardware design generation, spanning applications from classical digital circuits to quantum computing. Despite substantial progress in this direction, the quality of LLM-generated hardware design still cannot meet the requirements for practical deployment. In this work, we identify three critical challenges hindering the development of LLM-assisted hardware design generation: 1) limited data availability, 2) varied data quality, 3) inadequate inference-time efficiency. To address these fundamental challenges, this paper introduces a two-stage framework for AI-assisted hardware design by exploring decentralized training and personalized inference. In the first stage, we propose to harness private domain design sources through a hierarchical decentralized training mechanism that addresses data-sharing constraints. To mitigate the impact of low-quality data, we identify optimization opportunities in hardware generation tasks, using user-defined metrics for model aggregation. The second stage focuses on client personalization to enhance both speed and quality. We introduce a new metric, Trueput, to analyze LLM-assisted hardware generation efficiency. To optimize Trueput, we implement personalized inference-time acceleration and customized sampling strategies. Evaluating both classical and quantum benchmarks, our experimental results demonstrate that the proposed two-stage framework can significantly improve the model capability for hardware design generation. As orthogonal enhancements to existing methods, our framework can achieve 33% ~ 50% semantic accuracy improvement and 2.3 times speedup, depending on the difficulty of the generation tasks.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 46 of its 49 references that are in the map. First posted on arXiv 21 Apr 2025.

Builds on

46 of 49 references.

References · 46

  1. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2023Let's Verify Step by Stepcited by 3,789
  4. 2019Advances and Open Problems in Federated Learningcited by 3,046
  5. 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,218
  6. 2023Code Llama: Open Foundation Models for Codecited by 2,139
  7. 2024Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameterscited by 1,986
  8. 2022Editing Models with Task Arithmeticcited by 1,120
  9. 2024Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Headscited by 843
  10. 2023Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunchcited by 694
  11. 2024A Survey on Large Language Models for Code Generationcited by 658
  12. 2020Personalized Federated Learning with Moreau Envelopescited by 618
34 more
  1. 2024EAGLE: Speculative Sampling Requires Rethinking Feature Uncertaintycited by 578
  2. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 568
  3. 2023SpecInfer: Accelerating Generative Large Language Model Serving with Tree-based Speculative Inference and Verificationcited by 365
  4. 2020Think Locally, Act Globally: Federated Learning with Local and Global Representationscited by 325
  5. 2024Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunitiescited by 254
  6. 2021Federated Learning for Internet of Things: A Comprehensive Surveycited by 252
  7. 2023VerilogEval: Evaluating Large Language Models for Verilog Code Generationcited by 252
  8. 2024A Survey on Data Selection for Language Modelscited by 236
  9. 2023VeriGen: A Large Language Model for Verilog Code Generationcited by 200
  10. 2024Recursive Introspection: Teaching Language Model Agents How to Self-Improvecited by 165
  11. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 161
  12. 2023RTLLM: An Open-Source Benchmark for Design RTL Generation with Large Language Modelcited by 148
  13. 2020Survey of Personalization Techniques for Federated Learningcited by 145
  14. 2023What's In My Big Data?cited by 129
  15. 2023DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Modelscited by 123
  16. 2023ChipGPT: How far are we from natural language hardware designcited by 105
  17. 2023RTLFixer: Automatically Fixing RTL Syntax Errors with Large Language Modelscited by 100
  18. 2019Hierarchical Federated Learning Across Heterogeneous Cellular Networkscited by 98
  19. 2023Soft Merging of Experts with Adaptive Routingcited by 90
  20. 2024From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Futurecited by 86
  21. 2023RTLCoder: Outperforming GPT-3.5 in Design RTL Generation with Our Open-Source Dataset and Lightweight Solutioncited by 71
  22. 2024Knowledge Composition using Task Vectors with Learned Anisotropic Scalingcited by 37
  23. 2024AssertLLM: Generating and Evaluating Hardware Verification Assertions from Design Specifications via Multi-LLMscited by 31
  24. 2024Qiskit Code Assistant: Training LLMs for generating Quantum Computing Codecited by 25
  25. 2024HLSPilot: LLM-based High-Level Synthesiscited by 25
  26. 2024LLM4SecHW: Leveraging Domain Specific Large Language Model for Hardware Debuggingcited by 18
  27. 2024Hardware-Aware Parallel Prompt Decoding for Memory-Efficient Acceleration of LLM Inferencecited by 16
  28. 2025Enhancing LLM-based Quantum Code Generation with Multi-Agent Optimization and Quantum Error Correctioncited by 11
  29. 2025Exploring Code Language Models for Automated HLS-based Hardware Generation: Benchmark, Infrastructure and Analysiscited by 10
  30. 2024Federated Learning as a Service for Hierarchical Edge Networks with Heterogeneous Modelscited by 6
  31. 2024Are LLMs Any Good for High-Level Synthesis?cited by 6
  32. 2024Optimizing High-Level Synthesis Designs with Retrieval-Augmented Large Language Modelscited by 6
  33. 2025FW-Merging: Scaling Model Merging with Frank-Wolfe Optimizationcited by 5
  34. 2024Flight: A FaaS-Based Framework for Complex and Hierarchical Federated Learningcited by 3

Built on by

Not cited yet.