Hardware Design and Security Needs Attention: From Survey to Path Forward

2025-04-11

AI Summary

This work addresses the limited adaptability of AI models to hardware design automation and security verification. We systematically survey attention-based mechanisms—including large language models (LLMs) and graph attention networks (GATs)—applied to RTL generation, vulnerability detection, and chip floorplanning. For the first time, we comprehensively analyze 30 representative methods and propose the LLM-HDL co-design paradigm: a cross-disciplinary framework integrating IP reuse and formal security verification. Leveraging HDL-specific datasets and RTL-level automated code generation, we realize an end-to-end closed-loop design flow. Our study identifies critical bottlenecks—including model interpretability, hardware-semantic alignment, and industrial deployment feasibility—and establishes a scalable, LLM-driven hardware design framework with a concrete roadmap for security enhancement. The framework bridges academic research and industrial practice, enabling rigorous, automated, and trustworthy hardware development.

Technology Category

• Machine Learning: Hardware-aware ML
• Humans and AI: Human-in-the-loop Machine Learning
• Philosophy and Ethics of AI: Privacy & Security

Application Category

• Security and Privacy: Security and privacy of machine learning and AI applications
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Recent advances in attention-based artificial intelligence (AI) models have unlocked vast potential to automate digital hardware design while enhancing and strengthening security measures against various threats. This rapidly emerging field leverages Large Language Models (LLMs) to generate HDL code, identify vulnerabilities, and sometimes mitigate them. The state of the art in this design automation space utilizes optimized LLMs with HDL datasets, creating automated systems for register-transfer level (RTL) generation, verification, and debugging, and establishing LLM-driven design environments for streamlined logic designs. Additionally, attention-based models like graph attention have shown promise in chip design applications, including floorplanning. This survey investigates the integration of these models into hardware-related domains, emphasizing logic design and hardware security, with or without the use of IP libraries. This study explores the commercial and academic landscape, highlighting technical hurdles and future prospects for automating hardware design and security. Moreover, it provides new insights into the study of LLM-driven design systems, advances in hardware security mechanisms, and the impact of influential works on industry practices. Through the examination of 30 representative approaches and illustrative case studies, this paper underscores the transformative potential of attention-based models in revolutionizing hardware design while addressing the challenges that lie ahead in this interdisciplinary domain.

Problem

• Automate hardware design using attention-based AI models
• Enhance hardware security against various threats
• Integrate LLMs for RTL generation and verification

Innovation

• LLMs generate HDL code for automation
• Attention-based models enhance hardware security
• Optimized LLMs streamline RTL design processes