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SynthAI: A Multi Agent Generative AI Framework for Automated Modular HLS Design Generation

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arxiv 2405.16072 v4 pith:KCWGZGWS submitted 2024-05-25 cs.AI

classification cs.AI
keywords synthaidesigndesignsautomatedcomplexframeworkgenerationadhere
verification ladder T0 review T1 audit T2 compute T3 formal
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In this paper, we introduce SynthAI, a new method for the automated creation of High-Level Synthesis (HLS) designs. SynthAI integrates ReAct agents, Chain-of-Thought (CoT) prompting, web search technologies, and the Retrieval-Augmented Generation (RAG) framework within a structured decision graph. This innovative approach enables the systematic decomposition of complex hardware design tasks into multiple stages and smaller, manageable modules. As a result, SynthAI produces synthesizable designs that closely adhere to user-specified design objectives and functional requirements. We further validate the capabilities of SynthAI through several case studies, highlighting its proficiency in generating complex, multi-module logic designs from a single initial prompt. The SynthAI code is provided via the following repo: \url{https://github.com/sarashs/FPGA_AGI}

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Revolution or Hype? Seeking the Limits of Large Models in Hardware Design

    cs.LG 2025-09 conditional novelty 1.0 of 10

    Large models can help early-stage hardware design and verification, but their reliability, data, and precision limits mean traditional EDA algorithms and formal verification remain necessary.

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