Can Reasoning Models Reason about Hardware? An Agentic HLS Perspective

• URL: http://arxiv.org/abs/2503.12721v2
• Date: Mon, 14 Apr 2025 00:39:57 GMT
• Title: Can Reasoning Models Reason about Hardware? An Agentic HLS Perspective
• Authors: Luca Collini, Andrew Hennessee, Ramesh Karri, Siddharth Garg,
• Abstract summary: OpenAI o3-mini and DeepSeek-R1 use enhanced reasoning through Chain-of-Thought (CoT)<n>This paper investigates whether reasoning LLMs can address challenges in High-Level Synthesis (HLS) design space exploration and optimization.
• Score: 18.791753740931185
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent Large Language Models (LLMs) such as OpenAI o3-mini and DeepSeek-R1 use enhanced reasoning through Chain-of-Thought (CoT). Their potential in hardware design, which relies on expert-driven iterative optimization, remains unexplored. This paper investigates whether reasoning LLMs can address challenges in High-Level Synthesis (HLS) design space exploration and optimization. During HLS, engineers manually define pragmas/directives to balance performance and resource constraints. We propose an LLM-based optimization agentic framework that automatically restructures code, inserts pragmas, and identifies optimal design points via feedback from HLs tools and access to integer-linear programming (ILP) solvers. Experiments compare reasoning models against conventional LLMs on benchmarks using success rate, efficiency, and design quality (area/latency) metrics, and provide the first-ever glimpse into the CoTs produced by a powerful open-source reasoning model like DeepSeek-R1.

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