Harnessing Large Language Models Towards More Accessible Hardware Accelerator Design
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Zhang, Yongan
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Abstract
Today's computing landscape, increasingly dominated by computationally demanding algorithms such as deep learning, requires specialized hardware accelerators for efficiency. However, traditional hardware design processes rely heavily on expert-driven tasks like register-transfer-level (RTL) coding and optimization, resulting in lengthy development cycles and limited accessibility. This bottleneck widens the gap between rapid advancements in application domains and the comparatively slower evolution of supporting hardware. Meanwhile, Large Language Models (LLMs) are transforming technical domains like software engineering, by enabling the translation of natural language descriptions into functional code, raising the critical question: Can we similarly leverage LLMs to streamline hardware accelerator design into a flexible and broadly accessible process? Consequently, this thesis aims to reshape hardware design into a more automated and inclusive endeavor, allowing engineers, researchers, and students from diverse backgrounds to more easily and efficiently engage in hardware development tasks, including hardware coding, optimization, and debugging, through hardware-specialized AI assistants. Bridging LLM capabilities and hardware design practices could significantly democratize hardware development, shorten design iteration cycles, and enable hardware innovation to keep pace with rapidly evolving software and AI applications.
The aforementioned vision faces steep challenges. Despite vast training, current pretrained LLMs inherently lack expertise in specialized hardware semantics like Verilog, timing constraints, and synthesizability. Another critical hurdle is complexity management, as hardware design spans multiple stages—from high-level architecture to detailed implementation—necessitating frameworks enabling LLMs to approach problems step-by-step rather than attempting overwhelming tasks at once. Ensuring verification and correctness is also essential since hardware errors can lead to severe failures; thus, enabling LLM-generated designs to autonomously verify and self-correct their work presents significant difficulties. Additionally, the challenge of optimization and performance tuning means LLMs need quantitative feedback mechanisms to iteratively refine designs for timing, area, and power efficiency, integrating AI creativity with rigorous EDA analytics. Lastly, balancing generality versus specialization requires our methods to remain broadly applicable across various hardware accelerators yet adaptable enough to excel in specialized domains, such as AR/VR codec avatars, without sacrificing overall flexibility and usability.
To address these challenges, my thesis introduces a novel ecosystem leveraging LLMs to democratize and automate hardware accelerator design. The core idea is enabling LLMs to collaboratively tackle complex hardware design tasks through systematic task planning and decomposition, generation, verification, and iterative refinement processes. Specifically, the proposed ecosystem comprises three hierarchical workflow levels, with concrete demonstrations of system integration and optimization:
1. Design Planning and Generation: We developed GPT4AIGChip, a structured framework that equips LLMs to effectively plan for hardware designs tasks. This is achieved by breaking down complex tasks into well-defined stages, including architectural planning, module decomposition, and code generation. This modular approach ensures that resulting hardware designs remain manageable, contextually coherent, and practically viable for real-world applications. This level shows that LLMs, with structured guidance and training, can reliably produce complex hardware designs, marking a significant paradigm shift in leveraging more flexible format, such as natural language, for precise engineering tasks. 2. Hardware Knowledge Enhancement: Recognizing that existing LLMs often lack specific hardware design knowledge, we introduced MG-Verilog, an automated framework designed to generate multi-grained synthetic training data tailored for hardware design. MG-Verilog produces high-quality hardware design datasets through a novel Pyramid of Thoughts (PoT) data structure, enhanced by curated Retrieval-Augmented Generation (RAG) techniques, and automated validation. This structured dataset significantly enhances LLMs' capabilities, effectively addressing critical knowledge gaps across various hardware design scenarios, and substantially improves their performance in practical hardware design contexts through methods like fine-tuning and in-context learning. 3. Verification and Iterative Improvement: To ensure functional correctness and facilitate iterative design enhancements, we developed SLAVA, a scalable LLM-driven framework that introduces an assertion-guided automated self-refinement loop integrating RTL generation, verification, and iterative repair. Unlike earlier frameworks that focused on isolated assertion generation, SLAVA autonomously generates modular RTL, derives verification plans and SystemVerilog Assertions (SVAs), and leverages verification feedback to iteratively refine both RTL and SVAs. SLAVA intelligently analyzes verification outcomes to determine whether issues stem from genuine design errors or verification artifacts, employing advanced techniques such as systematic SVA source attribution, graph-based test-failure localization, and context-aware repair strategies. This comprehensive approach significantly enhances the robustness and accuracy of automated design refinement, effectively bridging the gap between design and verification stages. Beyond these three hierarchical workflow levels, this thesis also presents a comprehensive demonstration of practical system-level integration and optimization: To provide practical integration and optimizations of the LLM-driven for hardware design ecosystem, we introduced AutoAI2C and Re-CATA frameworks. AutoAI2C merges traditional hardware automation techniques and tool-chains with AI-driven approaches to optimize accelerator designs, focusing on real-world efficiency. Concurrently, Re-CATA showcases these integrated methodologies within a customizable, domain-specific accelerator tailored to AR/VR applications, illustrating the flexibility and applicability of the ecosystem in realistic scenarios. This practical demonstration provides a glimpse into a future where engineers collaborate seamlessly with AI to streamline and innovate hardware design.
A central theme across these levels is integration, where each level collaboratively complementing the others, enhancing LLM knowledge, generating hardware designs, verifying and refining these designs, optimizing them through conventional tools, and ultimately tailoring outputs for practical applications. In the long term, these techniques aim to transform hardware development, making it as rapid, iterative, and accessible as software development. This transformation could result in a highly adaptive hardware design process, enabling a broader range of developers to efficiently create specialized hardware tailored specifically to their application needs. Although ambitious, the work presented in this thesis represents a significant step toward achieving this vision of automated and accelerated hardware design. Last but least, rather than replacing human engineers, this approach seeks to elevate their roles to supervisory and creative positions, delegating routine tasks to AI. If successfully implemented, this synergy could drive greater innovation, shifting the primary challenge from technical execution to idea generation, allowing engineers to articulate objectives while AI autonomously produces optimized hardware solutions.
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Date
2025-05-19
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