AgentDSE: Reasoning-Augmented Architectural Design Space Exploration

2026-06-19

AI Summary

Traditional design space exploration (DSE) for computer architectures relies heavily on black-box simulation and lacks the reasoning capability of human experts who integrate physical constraints with workload structure. This work proposes a closed-loop simulation framework powered by a general-purpose large language model (LLM) agent, which—without any fine-tuning—automatically guides architectural DSE by emulating human architects’ reasoning processes. By integrating an LLM agent, closed-loop interaction with a simulator, automated code generation, and an interpretable architectural reasoning mechanism, the approach achieves comparable or superior design quality in tasks such as DNN accelerator mapping, hardware-software co-design, and CPU cache optimization, using only 1%–2% of the simulation budget required by conventional methods. Moreover, it produces traceable and auditable decision trajectories.

Abstract

Traditional architectural design space exploration (DSE) is highly inefficient, typically requiring tens of thousands of simulator evaluations across various optimization methods. This inefficiency arises because conventional methods treat the simulator as a black-box oracle. In contrast, human architects effectively guide exploration by reasoning through physical constraints, performance bottlenecks, data reuse, and workload structures. To bridge this gap, we introduce AgentDSE, a simulator-in-the-loop methodology driven by a general-purpose large language model (LLM) coding agent. AgentDSE automates this architectural-reasoning loop without requiring model fine-tuning, precomputed design databases, or domain-specific optimizer code. Across deep neural network (DNN) accelerator mapping, hardware/software co-design, and CPU cache-hierarchy optimization, AgentDSE achieves competitive or better design quality with up to two orders of magnitude fewer evaluations. AgentDSE also produces inspectable traces that surface architectural hypotheses, performance cliffs, implicit priors, and simulator artifacts, making every search decision traceable rather than buried in optimizer state.

Problem

• Design Space Exploration
• Architectural Reasoning
• Simulator Efficiency
• Black-box Optimization
• Hardware Design

Innovation

• Reasoning-Augmented DSE
• LLM Coding Agent
• Simulator-in-the-Loop
• Architectural Design Space Exploration
• Interpretable Optimization