SynthAI: A Multi Agent Generative AI Framework for Automated Modular HLS Design Generation
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866913513325199360 |
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| author | Sheikholeslam, Seyed Arash Ivanov, Andre |
| author_facet | Sheikholeslam, Seyed Arash Ivanov, Andre |
| contents | 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} |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_16072 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SynthAI: A Multi Agent Generative AI Framework for Automated Modular HLS Design Generation Sheikholeslam, Seyed Arash Ivanov, Andre Artificial Intelligence 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} |
| title | SynthAI: A Multi Agent Generative AI Framework for Automated Modular HLS Design Generation |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2405.16072 |