Agentic AI Automates RTL Waveform Analysis And Debugging (Columbia, Google)
Agentic AI Automates RTL Waveform Analysis And Debugging (Columbia, Google)
Researchers from Columbia University, Google DeepMind, and Google have published a paper proposing Back-to-the-Future (BTTF), an end-to-end agentic framework that automates post-simulation waveform debugging in chip verification.
Researchers at Columbia University, Google DeepMind, and Google published a technical paper titled “Back to the Future: Rethinking EDA Infrastructure for Agentic Systems in Chip Design Verification.”
Abstract
“The unprecedented computational scale of modern artificial intelligence depends on complex, multi-billion-transistor Systems-on-Chip, yet the workflows that verify these chips remain stubbornly manual. Although Large Language Models (LLMs) have made rapid inroads into Electronic Design Automation (EDA), approximately 74.6% of existing studies target static Register-Transfer Level (RTL) code generation, leaving post-simulation verification and interactive waveform debugging largely untouched. We introduce Back-to-the-Future (BTTF), an end-to-end agentic framework that closes this infrastructural gap. BTTF distills massive, unstructured simulation dumps into a normalized relational SQLite database and couples it with a collaborative multi-agent orchestration engine that translates natural-language verification queries into schema-aware SQL while correlating signal anomalies with versioned RTL repositories. Across a 150-query benchmark, BTTF attains 95.33% execution accuracy, charting a practical path toward autonomous EDA verification.”
Find the technical paper here. October 2026.
Yang, Je, Ivan Lobov, and Thomas Karpati. “Back to the Future: Rethinking EDA Infrastructure for Agentic Systems in Chip Design Verification.” arXiv preprint arXiv:2610.06790 (2026).
Source: Semiconductor Engineering Chips and Packaging · semiengineering.com