Can AI Agents Design Better Hardware?

Author: Denis Avetisyan

A new approach uses teams of AI-powered agents to automatically optimize hardware designs, pushing the boundaries of performance and efficiency.

The system dissects a hardware design [latex]\mathcal{D}[/latex] into its functional components, deploying a swarm of optimizer agents-each focused on a sub-function-to explore performance trade-offs between latency and area, then leverages integer linear programming to identify top-performing combinations before subjecting them to further, iterative refinement by exploration agents, ultimately yielding a fully optimized design [latex]\mathcal{D}^{\ast}[/latex].

This review examines the potential of scalable agent-based optimization for high-level synthesis, leveraging large language model agents to explore hardware design spaces and improve latency and area.

Achieving optimal hardware designs remains challenging despite advancements in High-Level Synthesis (HLS), often requiring extensive manual effort and domain expertise. This paper, ‘Agent Factories for High Level Synthesis: How Far Can General-Purpose Coding Agents Go in Hardware Optimization?’, introduces a scalable agent-based optimization framework leveraging large language models to explore the design space and automatically improve hardware performance. Experiments demonstrate that increasing the number of agents can yield a mean [latex]8.27\times[/latex] speedup, with significant gains on complex benchmarks, while consistently rediscovering known optimization patterns without specialized training. Could this approach unlock a new era of autonomous hardware design and accelerate the development of efficient, tailored FPGA implementations?

Deconstructing Complexity: The Hardware Design Challenge

The pursuit of high-performance hardware for modern applications-from artificial intelligence to real-time data processing-necessitates navigating an immense design space. This space encompasses countless configurations of logic gates, memory architectures, and interconnection networks, quickly overwhelming the capacity of human designers. Even seemingly simple tasks can yield a combinatorial explosion of possibilities, making exhaustive manual exploration impractical. The sheer scale of this problem demands automated techniques capable of efficiently sampling and evaluating potential designs, identifying optimal solutions that would remain undiscovered through traditional, human-driven approaches. Consequently, research increasingly focuses on algorithms and tools that can intelligently search this vast landscape, balancing competing objectives like speed, power consumption, and resource utilization to deliver truly innovative hardware implementations.

The pursuit of high-performance hardware often presents a critical balancing act between competing design goals. Traditional methodologies, relying heavily on manual optimization and iterative refinement, increasingly falter when confronted with the sheer complexity of modern systems. Specifically, reducing latency – the delay in processing data – frequently demands increased circuit area, which in turn elevates manufacturing costs and power consumption. Conversely, minimizing area often necessitates architectural compromises that negatively impact processing speed. This inherent trade-off between latency and area, compounded by other metrics like power usage and resource allocation, creates a design space too vast for exhaustive manual exploration, effectively slowing the pace of innovation and preventing the realization of truly optimized hardware solutions. Consequently, engineers are compelled to seek automated techniques capable of navigating these complex relationships and identifying designs that achieve the most favorable balance for specific application requirements.

The escalating sophistication of field-programmable gate array (FPGA) designs necessitates a shift towards automated hardware exploration. Modern FPGAs, capable of implementing increasingly complex algorithms, present a design space so vast that manual optimization becomes impractical, if not impossible. Researchers are developing automated techniques – including evolutionary algorithms, reinforcement learning, and Bayesian optimization – to systematically search for optimal hardware implementations that balance competing objectives like performance, power consumption, and resource utilization. These methods allow designers to explore a multitude of configurations, identify promising architectures, and ultimately realize FPGA-based systems that would be unattainable through traditional manual approaches, driving innovation in areas such as artificial intelligence, high-performance computing, and embedded systems.

The agent leverages data flow graph analysis to automatically generate Integer Linear Programming (ILP) constraints that minimize latency while respecting area constraints, as demonstrated using a graph from [7].

The Agent Factory: Automating Hardware Synthesis

The Agent Factory is a software framework designed to automatically explore the High-Level Synthesis (HLS) design space through the generation of multiple, independent autonomous agents. These agents operate concurrently, each tasked with investigating different configurations of HLS directives to optimize a given hardware design. The framework facilitates parallel exploration, increasing the efficiency of the optimization process by enabling simultaneous testing of various design choices. Agent autonomy is central to the Factory’s operation; each agent makes independent decisions regarding directive application and evaluation, without requiring direct human intervention. This parallel, automated approach aims to identify optimal or near-optimal HLS configurations more rapidly than traditional manual optimization methods.

The agent factory employs Large Language Model (LLM)-based methods to automate High-Level Synthesis (HLS) optimization by intelligently suggesting and evaluating HLS directives. This automation involves the LLM analyzing the given hardware design context – including code, constraints, and target architecture – to propose directives aimed at improving performance or resource utilization. The agents then evaluate the impact of these directives, typically through simulation or synthesis, and iteratively refine their suggestions based on the observed results. This process eliminates the need for manual exploration of the HLS directive space, significantly reducing development time and potentially discovering optimization strategies that might be overlooked by human designers.

The agent factory utilizes Opus 4.5 and 4.6, a large language model, as its primary directive generator. Given a High-Level Synthesis (HLS) design context – encompassing source code, performance constraints, and target hardware – Opus 4.5/4.6 outputs suggested HLS directives. These directives aim to optimize the design for metrics such as latency, throughput, and resource usage. The LLM is prompted with information detailing the design’s functionality and the desired optimization goals, enabling it to propose specific directives, including loop unrolling factors, pipeline initiation intervals, and dataflow configurations. The generated directives are then evaluated within the HLS toolchain to assess their impact on the design’s performance and resource utilization.

The agents within the Agent Factory utilize Large Language Models (LLMs) to address the inherent multi-objective optimization problem present in High-Level Synthesis (HLS). HLS design space exploration requires balancing competing goals, specifically maximizing performance – typically throughput and latency – while minimizing resource utilization, including logic elements, memory, and power consumption. LLMs enable efficient navigation of these trade-offs by predicting the impact of different HLS directives on both performance and resource metrics. This predictive capability allows agents to prioritize directive suggestions that offer the most favorable balance, effectively searching for Pareto-optimal solutions within the design space without exhaustive and computationally expensive evaluation of all possible configurations.

Average inference cost remains consistent across scaling sessions utilizing either Opus 4.5 or 4.6.

Mapping the Terrain: Functional Insights for Guided Exploration

The Agent Factory employs a Function Call Graph (FCG) to represent the dependencies and relationships within a hardware design’s code. This graph is a static analysis of the code, identifying all function calls and the data flow between them. Nodes in the FCG represent individual functions, while directed edges denote calls from one function to another. The FCG allows the agent to determine the scope and potential impact of modifications to any given function, enabling targeted optimizations and avoiding unintended consequences. Construction of the FCG involves parsing the hardware description language (HDL) and resolving all function instantiation and call sites, resulting in a comprehensive map of the design’s functional organization.

The Function Call Graph serves as the primary input for the agent’s optimization strategy by mapping dependencies between functions within the hardware design. This allows the agent to identify and prioritize code paths that contribute most significantly to overall performance; modifications to functions with high call frequency or those situated on critical paths are given precedence. The agent leverages this information to assess the potential impact of each optimization, focusing resources on changes that are likely to yield substantial improvements in execution speed and efficiency, rather than exploring less impactful modifications. This prioritization is essential for navigating the large design space and converging on optimal solutions within a reasonable timeframe.

Functional Correctness Testing is a core component of the automated design space exploration process. Each generated design variant undergoes rigorous testing to verify that its functionality matches the original specification before performance is evaluated. This testing suite includes a comprehensive set of test cases designed to cover all specified behaviors, and any variant failing these tests is immediately discarded, preventing the evaluation of incorrect or non-functional designs. The integration of this testing within the exploration loop ensures that only valid designs contribute to the final optimization, maintaining design integrity throughout the process.

The integration of functional analysis and automated hardware exploration yields substantial performance gains. Specifically, designs generated through this combined methodology demonstrate a mean speedup of 8.27x when compared to baseline, manually-created designs. This improvement is achieved by leveraging functional insights to guide the exploration process, focusing computational resources on optimizations with the highest potential impact on hardware performance. The methodology systematically evaluates numerous design variants, ensuring that performance gains are realized without compromising functional correctness.

Increasing the number of expert agents improves latency by a factor ranging from 1.4[latex] imes[/latex] to 14.5[latex] imes[/latex] across six benchmarks, with gains typically plateauing after four agents.

Refining the Blueprint: Fine-Grained Control with HLS Directives

The Agent Factory employs High-Level Synthesis (HLS) directives – notably Pipeline, Unroll, and Array Partition – as a means of precisely tailoring hardware implementations for optimal performance. These directives function as instructions to the HLS compiler, guiding it to restructure the code for improved parallelism and efficiency. Pipeline allows for the overlapping of operations, increasing throughput, while Unroll replicates loop bodies to expose more opportunities for concurrent execution. Critically, Array Partition breaks down large arrays into smaller, more manageable segments, thereby reducing memory access bottlenecks and enabling faster data retrieval. By intelligently applying these directives, the system achieves significant gains in hardware performance and resource utilization, effectively bridging the gap between software algorithms and optimized hardware designs.

The Agent Factory employs Large Language Model (LLM) agents to strategically apply High-Level Synthesis (HLS) directives, fundamentally reshaping how hardware operates. These agents don’t simply apply directives randomly; instead, they intelligently analyze the code and target specific bottlenecks in memory access and computational flow. By optimizing memory access patterns – for instance, by partitioning arrays to allow parallel reads and writes – and enabling parallel execution through techniques like pipelining and unrolling, the LLM agents unlock significant performance gains. This targeted approach ensures that the generated hardware isn’t just faster, but also more efficient in utilizing available resources, leading to substantial speedups in critical kernels like streamcluster, kmeans, and lavamd.

Following the LLM-driven generation of hardware designs through High-Level Synthesis, rigorous verification and refinement are crucial. Logic synthesis tools, such as ABC, play a vital role in this process by transforming the abstract high-level code into a concrete gate-level netlist. This netlist is then subjected to extensive analysis and optimization; ABC, for instance, employs techniques like Boolean simplification and structural optimization to minimize area, reduce power consumption, and enhance performance. The resulting design undergoes further checks to ensure functional correctness and adherence to specified constraints, ultimately yielding a hardware implementation ready for deployment and integration into larger systems. This post-synthesis refinement is essential for bridging the gap between algorithmic description and efficient hardware realization.

Performance gains achieved through the Agent Factory’s collaborative approach to hardware design are substantial, with benchmark results demonstrating significant speedups across various kernels. Specifically, scaling the number of LLM agents revealed a maximum 20x acceleration for the streamcluster kernel, a computationally intensive algorithm used in data analysis. The kmeans kernel, frequently employed in clustering applications, experienced a 10x performance boost, while the lavamd kernel, central to sparse matrix operations, benefited from an 8x speedup. These results highlight the potential of AI-driven hardware co-design to dramatically improve the efficiency of specialized computing tasks, pushing the boundaries of what is achievable with modern hardware acceleration.

Increasing the number of agents generally improves performance across benchmarks, extending the Pareto front towards lower latency and more favorable area-latency trade-offs, particularly for challenging problems like streamcluster, leukocyte, and NW.

Scaling the Collective: Future Directions in Automated Hardware Design

The capacity to scale the number of autonomous agents within a problem-solving framework fundamentally expands the types of challenges it can address. As design spaces grow in dimensionality and complexity – encompassing more variables, constraints, and potential interactions – a larger agent population becomes critical for thorough exploration. This scaling isn’t merely about increasing computational power; it’s about enabling a more robust and diversified search for optimal solutions. With each added agent, the framework gains a greater ability to navigate intricate landscapes, overcome local optima, and ultimately discover designs that would remain inaccessible to limited-agent systems. This approach suggests that the true potential of LLM-based agents lies not just in their individual capabilities, but in their collective intelligence when orchestrated at scale.

The framework’s ability to discover effective solutions is directly correlated with the scale of its agent population. Increasing the number of autonomous agents engaged in the design exploration process fosters a more robust search of the problem space. This expanded exploration isn’t simply about covering more ground; it’s about enhancing the likelihood of identifying truly optimal solutions that might remain hidden to a smaller, less diverse group. A larger agent population introduces greater redundancy, allowing the system to overcome local optima and navigate complex challenges with increased resilience, ultimately leading to more innovative and effective designs.

Each execution of the LLM-based agents currently requires an average of 7.67 million tokens, a figure that directly informs the computational expense associated with this approach. This substantial token usage highlights both the power and the current limitations of leveraging large language models for complex problem-solving; while the agents demonstrate a capacity for nuanced exploration, the associated cost necessitates ongoing research into optimization strategies. Understanding this metric is crucial for assessing the scalability of the framework and for identifying areas where efficiency gains can be realized, ultimately enabling the application of these agents to an even wider range of challenging design spaces.

Ongoing development prioritizes minimizing token consumption to enhance the practical applicability of these LLM-based agents. Current analyses reveal an average of 7.67 million tokens utilized per operational run, a figure that, while yielding promising results, presents a barrier to broader implementation and scalability. Researchers are actively investigating strategies such as knowledge distillation, prompt engineering, and architectural refinements to reduce this computational load without compromising solution quality. Diminishing token usage will not only lower operational costs but also facilitate the deployment of these agents in resource-constrained environments and enable the exploration of even larger and more intricate design spaces, ultimately broadening their impact across diverse applications.

The pursuit of optimized hardware, as demonstrated in this study of agent-based optimization, inherently involves challenging established boundaries. It’s a process of systematically dismantling assumptions to reveal underlying inefficiencies. As Ken Thompson observed, “Debugging is twice as hard as writing the code in the first place. Therefore, if you write the code as cleverly as possible, you are, by definition, not smart enough to debug it.” This sentiment resonates deeply with the approach detailed in the paper; the ‘Agent Factory’ doesn’t simply refine existing designs, but actively probes the design space, exposing limitations and ‘bugs’ – design sins, if you will – through exhaustive exploration. The more agents employed, the more thoroughly the system is stressed, and the more opportunities for uncovering hidden weaknesses are revealed, ultimately leading to more robust and optimized hardware.

Beyond the Factory Gates

The notion of an ‘Agent Factory’ – a system that scales optimization through sheer computational redundancy – reveals a fundamental truth about design exploration: brute force, when intelligently directed, often outpaces elegance. The improvements in latency and area demonstrated here are not merely incremental; they are a consequence of abandoning the search for a single ‘best’ solution in favor of a population of ‘good enough’ ones. Yet, this success merely highlights the limitations of current methods. The agents, reliant on Integer Linear Programming and guided by Large Language Models, are still fundamentally tethered to representations of the hardware, not the hardware itself.

The true challenge lies in moving beyond symbolic manipulation. Future iterations must grapple with the inherent messiness of physical reality-power dissipation, timing variations, and the subtle interplay of materials. One suspects that the most significant gains will come not from refining the agents’ coding skills, but from equipping them with a more visceral understanding of the constraints they are attempting to satisfy. Perhaps, the next ‘Agent Factory’ will simulate not just behavior, but also failure-learning from catastrophic designs with a speed no human engineer could match.

Ultimately, this work suggests that the design process is less about invention and more about efficient demolition. Each failed iteration, each suboptimal design, narrows the solution space. The factory doesn’t create optimal hardware; it systematically eliminates the non-optimal. And that, ironically, is a profoundly conservative principle, masquerading as innovation.

Original article: https://arxiv.org/pdf/2603.25719.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/