Agent Factories for High Level Synthesis: How Far Can General-Purpose Coding Agents Go in Hardware Optimization?

1. 1
   Understand Hardware Optimization with HLS

   Familiarize yourself with High-Level Synthesis (HLS) and its role in translating high-level algorithmic specifications into hardware designs. Identify common optimization targets like performance, area, and power consumption.
2. 2
   Define General-Purpose AI Agent Capabilities

   Outline the capabilities of general-purpose coding agents that can understand, analyze, and modify code (e.g., Python, C++ for HLS tools) to achieve optimization goals without explicit hardware-specific training.
3. 3
   Design a Two-Stage Agent Factory Pipeline

   Architect an 'agent factory' with a two-stage pipeline. The first stage typically focuses on initial broad optimizations or transformations, while the second stage handles refinement, verification, or further specialized adjustments.
4. 4
   Orchestrate Autonomous Optimization Agents

   Implement mechanisms to construct and coordinate multiple autonomous AI agents within your factory. Assign specific roles or tasks to agents in each stage, ensuring their outputs feed into subsequent steps or agents.
5. 5
   Evaluate Agent Performance on HLS Tasks

   Develop a methodology to empirically assess how well your general-purpose agents, orchestrated by the factory, optimize hardware designs from high-level specifications. Measure metrics like latency, throughput, resource utilization, and power.

class OptimizationAgent:
    def __init__(self, name):
        self.name = name

    def optimize(self, design_spec):
        # Placeholder for agent's optimization logic (e.g., code transformation, parameter tuning)
        print(f"Agent {self.name} optimizing design spec: {design_spec[:30]}...")
        # In a real scenario, this would involve LLM calls, static analysis, etc.
        return f"Optimized_{self.name}_{design_spec}"

class AgentFactory:
    def __init__(self):
        self.stage1_agents = []
        self.stage2_agents = []

    def add_agent(self, stage, agent):
        if stage == 1:
            self.stage1_agents.append(agent)
        elif stage == 2:
            self.stage2_agents.append(agent)
        else:
            raise ValueError("Stage must be 1 or 2")

    def run_pipeline(self, initial_design_spec):
        print("\n--- Running Stage 1: Initial Optimization ---")
        current_design = initial_design_spec
        for agent in self.stage1_agents:
            current_design = agent.optimize(current_design)
            print(f"  -> Current state after {agent.name}: {current_design[:30]}...")

        print("\n--- Running Stage 2: Refinement & Coordination ---")
        final_design = current_design
        for agent in self.stage2_agents:
            final_design = agent.optimize(final_design)
            print(f"  -> Current state after {agent.name}: {final_design[:30]}...")
        
        return final_design

# --- Example Usage (Conceptual) ---
if __name__ == "__main__":
    factory = AgentFactory()
    
    # Add agents to Stage 1
    factory.add_agent(1, OptimizationAgent("InitialCodeRefactor"))
    factory.add_agent(1, OptimizationAgent("LoopUnroller"))

    # Add agents to Stage 2
    factory.add_agent(2, OptimizationAgent("ResourceAllocator"))
    factory.add_agent(2, OptimizationAgent("TimingConstraintAdjuster"))
    factory.add_agent(2, OptimizationAgent("PowerOptimizer"))

    high_level_spec = "def matrix_multiply(A, B): ... # HLS-friendly Python/C++ code"
    print(f"\nInitial High-Level Specification: {high_level_spec[:50]}...")
    optimized_hw_design = factory.run_pipeline(high_level_spec)
    print(f"\nFinal Optimized Hardware Design: {optimized_hw_design[:50]}...")

• Paperarxiv.org