4 papers
Can Agents Design Better Chips with a Higher Level Abstraction?
Zijian Ding, Yang Zou, Yizhou Sun +1
Large Language Model (LLM) agents are increasingly being explored for chip design, but most existing approaches operate directly at RTL. We ask whether agents can design better chi…
AgRefactor: Self-Evolving Agentic Workflow for HLS Compatibility and Performance
Yang Zou, Zijian Ding, Yizhou Sun +1
High-Level Synthesis (HLS) provides a fast path from concepts to silicon, but converting real-world software into synthesizable HLS code remains challenging due to restrictive lang…
MM-ReCoder: Advancing Chart-to-Code Generation with Reinforcement Learning and Self-Correction
Zitian Tang, Xu Zhang, Jianbo Yuan +4
Multimodal Large Language Models (MLLMs) have recently demonstrated promising capabilities in multimodal coding tasks such as chart-to-code generation. However, existing methods pr…
BRIDGES: Bridging Graph Modality and Large Language Models within EDA Tasks
Wei Li, Yang Zou, Christopher Ellis +3
While many EDA tasks already involve graph-based data, existing LLMs in EDA primarily either represent graphs as sequential text, or simply ignore graph-structured data that might…