NLS: Natural-Level Synthesis for Hardware Implementation Through GenAI

• URL: http://arxiv.org/abs/2504.01981v1
• Date: Fri, 28 Mar 2025 15:46:01 GMT
• Title: NLS: Natural-Level Synthesis for Hardware Implementation Through GenAI
• Authors: Kaiyuan Yang, Huang Ouyang, Xinyi Wang, Bingjie Lu, Yanbo Wang, Charith Abhayaratne, Sizhao Li, Long Jin, Tiantai Deng,
• Abstract summary: This paper introduces Natural-Level Synthesis, an innovative approach for generating hardware using generative artificial intelligence on both the system level and component-level.<n>With NLS, engineers can participate more deeply in the development, synthesis, and test stages by using Gen-AI models to convert natural language descriptions directly into Hardware Description Language code.<n>We developed the NLS tool to facilitate natural language-driven HDL synthesis, enabling rapid generation of system-level HDL designs while significantly reducing development complexity.
• Score: 41.03569272854125
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces Natural-Level Synthesis, an innovative approach for generating hardware using generative artificial intelligence on both the system level and component-level. NLS bridges a gap in current hardware development processes, where algorithm and application engineers' involvement typically ends at the requirements stage. With NLS, engineers can participate more deeply in the development, synthesis, and test stages by using Gen-AI models to convert natural language descriptions directly into Hardware Description Language code. This approach not only streamlines hardware development but also improves accessibility, fostering a collaborative workflow between hardware and algorithm engineers. We developed the NLS tool to facilitate natural language-driven HDL synthesis, enabling rapid generation of system-level HDL designs while significantly reducing development complexity. Evaluated through case studies and benchmarks using Performance, Power, and Area metrics, NLS shows its potential to enhance resource efficiency in hardware development. This work provides a extensible, efficient solution for hardware synthesis and establishes a Visual Studio Code Extension to assess Gen-AI-driven HDL generation and system integration, laying a foundation for future AI-enhanced and AI-in-the-loop Electronic Design Automation tools.

Related papers

• Exploring Code Language Models for Automated HLS-based Hardware Generation: Benchmark, Infrastructure and Analysis [14.458529723566379]
  Large language models (LLMs) can be employed for programming languages such as Python and C++.<n>This paper explores leveraging LLMs to generate High-Level Synthesis (HLS)-based hardware design.
  arXiv  Detail & Related papers  (2025-02-19T17:53:59Z)
• Optimizing AI-Assisted Code Generation [0.8901073744693314]
  AI-assisted code-generation tools have significantly transformed software development. The security, reliability, functionality, and quality of the generated code must be guaranteed. This paper examines the implementation of these goals to date and explores strategies to optimize them.
  arXiv  Detail & Related papers  (2024-12-14T20:14:44Z)
• HiVeGen -- Hierarchical LLM-based Verilog Generation for Scalable Chip Design [55.54477725000291]
  HiVeGen is a hierarchical Verilog generation framework that decomposes generation tasks into hierarchical submodules.<n> automatic Design Space Exploration (DSE) into hierarchy-aware prompt generation, introducing weight-based retrieval to enhance code reuse.<n>Real-time human-computer interaction to lower error-correction cost, significantly improving the quality of generated designs.
  arXiv  Detail & Related papers  (2024-12-06T19:37:53Z)
• Specifications: The missing link to making the development of LLM systems an engineering discipline [65.10077876035417]
  We discuss the progress the field has made so far-through advances like structured outputs, process supervision, and test-time compute.<n>We outline several future directions for research to enable the development of modular and reliable LLM-based systems.
  arXiv  Detail & Related papers  (2024-11-25T07:48:31Z)
• Are LLMs Any Good for High-Level Synthesis? [1.3927943269211591]
  Large Language Models (LLMs) can streamline or replace the High-Level Synthesis (HLS) process. LLMs can understand natural language specifications and translate C code or natural language specifications. This study aims to illuminate the role of LLMs in HLS, identifying promising directions for optimized hardware design in applications such as AI acceleration, embedded systems, and high-performance computing.
  arXiv  Detail & Related papers  (2024-08-19T21:40:28Z)
• Meaning-Typed Programming: Language-level Abstractions and Runtime for GenAI Applications [8.308424118055981]
  Software is rapidly evolving from logical code to neuro-integrated applications that leverage generative AI and large language models (LLMs) for application functionality.<n>This paper proposes meaning-typed programming (MTP), a novel approach to simplify the creation of neuro-integrated applications.
  arXiv  Detail & Related papers  (2024-05-14T21:12:01Z)
• AutoHLS: Learning to Accelerate Design Space Exploration for HLS Designs [10.690389829735661]
  This paper proposes a novel framework called AutoHLS, which integrates a deep neural network (DNN) with Bayesian optimization (BO) to accelerate HLS hardware design optimization. Our experimental results demonstrate up to a 70-fold speedup in exploration time.
  arXiv  Detail & Related papers  (2024-03-15T21:14:44Z)
• Using the Abstract Computer Architecture Description Language to Model AI Hardware Accelerators [77.89070422157178]
  Manufacturers of AI-integrated products face a critical challenge: selecting an accelerator that aligns with their product's performance requirements. The Abstract Computer Architecture Description Language (ACADL) is a concise formalization of computer architecture block diagrams. In this paper, we demonstrate how to use the ACADL to model AI hardware accelerators, use their ACADL description to map DNNs onto them, and explain the timing simulation semantics to gather performance results.
  arXiv  Detail & Related papers  (2024-01-30T19:27:16Z)
• LLM4EDA: Emerging Progress in Large Language Models for Electronic Design Automation [74.7163199054881]
  Large Language Models (LLMs) have demonstrated their capability in context understanding, logic reasoning and answer generation. We present a systematic study on the application of LLMs in the EDA field. We highlight the future research direction, focusing on applying LLMs in logic synthesis, physical design, multi-modal feature extraction and alignment of circuits.
  arXiv  Detail & Related papers  (2023-12-28T15:09:14Z)
• Instructed Language Models with Retrievers Are Powerful Entity Linkers [87.16283281290053]
  Instructed Generative Entity Linker (INSGENEL) is the first approach that enables casual language models to perform entity linking over knowledge bases. INSGENEL outperforms previous generative alternatives with +6.8 F1 points gain on average.
  arXiv  Detail & Related papers  (2023-11-06T16:38:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.