This paper introduces PDAGENT-BENCH, a comprehensive benchmark for evaluating Large Language Models and vision-language models in the physical design stack of Very Large-Scale Integrated Circuits.

The paper introduces HORIZON, a self-evolving agent framework for hardware design using git operations.

HighTide is an evolving, AI-assisted, open-source benchmark suite for VLSI design, providing a comprehensive and scalable platform for hardware development.

The paper introduces VHDLSuite, an infrastructure for evaluating Large Language Models in VHDL, including a data pipeline, benchmark, and evaluation framework.

This paper evaluates AI agent systems for electronic design automation (EDA) using a unified benchmark called FluxBench, assessing their performance across various EDA workflows and tasks.

This paper presents CircuitWeave, a framework that extracts topology and behavior contracts from text and schematics, fuses them into a circuit contract, and generates RTL from this contract.

The paper introduces ProofLoop, a novel ReAct agent that uses a solver-in-the-loop approach to automatically generate and formally verify SystemVerilog Assertions (SVA) from natural language specifica…

The paper argues that current 'on-the-fly' AI agent design lacks necessary software engineering rigor and proposes an 'AI Workflow Store' to provide hardened, reusable, and reliable agent workflows.

This paper introduces ArchEval, a benchmark and platform for evaluating LLM agents on computer architecture design and optimization.

The paper introduces alignment contracts, a formal framework for specifying and enforcing behavioral constraints over observable effect traces, ensuring that powerful agentic security systems operate…

Introduce Parallel-Synthesis, a framework enabling a synthesizer to directly consume parallel agent branches' KV caches, improving efficiency and performance.

This review analyzes the dual impact of integrating Large Language Models (LLMs) into hardware design, detailing both their transformative potential in EDA and the critical security vulnerabilities th…

The paper introduces Phoenix, a training-free multi-agent framework that detects code vulnerabilities by synthesizing project-specific behavioral contracts, significantly outperforming existing method…

The paper introduces E-Bench, a synthetic benchmark for evaluating multi-step tool use in Large Language Models across three product domains.

This paper evaluates the performance of large language models on popular benchmarks and finds that only a small percentage of the performant implementations are significantly faster than canonical sol…

A new HLS tool is presented that enables fine-grained pipeline control in a sequential programming model for competitive PPA.

This paper presents an approach for turning exploratory large language model prototypes into auditable applications with traceable, auditable architecture.

The paper introduces CASS-RTL, a novel, model-agnostic framework that enhances the functional correctness of Large Language Models (LLMs) generating Register-Transfer Level (RTL) code by leveraging th…

The paper introduces LACUNA, a novel programming model that allows LLM agents to write code that shapes the runtime environment while maintaining strong type-checking safety guarantees.

The paper introduces a data-centric optimization pipeline to improve coding agents' ability to interact with a branching lakehouse, showing significant accuracy gains by treating agent evaluation as a…