Agentic AI Accelerator Design with Simulation-in-the-Loop

Core Tech Python, LLM Agents

Domain Architecture Simulation

Focus Design Space Exploration

The Premise

Designing custom hardware accelerators for rapidly evolving transformer models is traditionally a slow, human-in-the-loop process requiring extensive domain expertise. The challenge lies in navigating an immense design space encompassing compute arrays, memory hierarchies, and intricate interconnect topologies.

This project introduces a closed-loop optimization framework: an AI-agent pipeline that autonomously proposes, evaluates, and refines accelerator architectures. By integrating LLM-based hardware reasoning directly with cycle-accurate architecture simulation, the system creates a self-improving design engine tailored specifically for transformer workloads.

System Diagram v1.0 architecture

Core Mechanisms

memory

LLM-Based Hardware Reasoning

Leveraging large language models as heuristic search agents capable of understanding hardware constraints. The LLM digests target workload specifications (e.g., attention head dimensions, sequence lengths) and proposes initial architectural parameters, translating high-level model requirements into structural hardware concepts.

Prompt Engineering Heuristic Search

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Simulation Feedback Loop

Proposed architectures are codified and evaluated in a cycle-accurate simulator. The resulting performance metrics (latency, energy, area utilization) are fed back to the LLM agent, which uses this concrete grounding to iteratively refine compute arrays and memory hierarchies.

Design Space Exploration Results

The automated pipeline successfully identified non-intuitive interconnect topologies that outperformed standard mesh networks for specific transformer configurations. By iterating thousands of times without human intervention, the system mapped Pareto-optimal frontiers balancing compute density against memory bandwidth requirements.

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