Small Models, Big Designs: The Rise of Agentic AI in Hardware

Author: Denis Avetisyan

New research reveals that smaller AI models, empowered by intelligent agent frameworks, can rival the performance of their larger counterparts in automating complex hardware design tasks.

A streamlined chip-design workflow within fabless semiconductor organizations leverages task-specific skill learning models-particularly effective for beginner-level tasks-integrated into an agentic-AI framework to achieve automation through clearly defined objectives, workflows, and evaluation metrics, demonstrating that experience level-measured in years-influences the efficacy of this approach.

This review demonstrates that agentic small language models achieve competitive RTL generation performance on the CVDP benchmark, offering a path toward more energy-efficient AI-assisted hardware design.

The increasing computational demands of large language models present a paradox for sustainable AI development, particularly in specialized domains. This work, titled ‘David vs. Goliath: Can Small Models Win Big with Agentic AI in Hardware Design?’, investigates whether smaller models can rival the performance of their larger counterparts when applied to complex hardware design tasks. Results demonstrate that strategically deploying small language models within a curated agentic framework-facilitating task decomposition and iterative refinement-yields near-LLM performance at a significantly reduced computational cost. Could this approach unlock a future of efficient, adaptive, and ecologically sound AI-assisted design workflows?

The Inevitable Convergence: Demand, Efficiency, and the Algorithmic Imperative

The semiconductor industry currently navigates a landscape defined by escalating demands for both rapid innovation and environmental responsibility. Moore’s Law, while slowing, continues to drive expectations for increasingly powerful and efficient chips, requiring constant breakthroughs in design and materials. Simultaneously, concerns regarding the energy consumption and environmental impact of chip manufacturing are intensifying, pushing companies to adopt more sustainable practices. This confluence of factors creates significant pressure; manufacturers must not only deliver performance gains but also minimize their carbon footprint and resource utilization. Consequently, the industry is actively exploring new technologies and methodologies, including artificial intelligence, to address these dual challenges and secure a future marked by both progress and sustainability.

The relentless drive for more powerful and efficient computing has placed immense strain on conventional hardware design processes. Historically, creating new semiconductor chips relied heavily on manual effort, iterative simulations, and expert intuition – a methodology increasingly unable to keep pace with escalating complexity and shrinking design windows. This traditional approach now represents a significant bottleneck, as the time and resources required to bring a chip from concept to production have grown exponentially. The sheer number of possible design configurations, coupled with the need for meticulous verification, makes exhaustive manual exploration impractical. Consequently, the industry is actively seeking automated solutions, recognizing that accelerating innovation necessitates a shift towards algorithms and machine learning capable of navigating this vast design space and optimizing performance with greater speed and precision.

Artificial intelligence is rapidly transforming hardware design by automating traditionally manual and time-consuming processes. These AI-powered systems excel at tasks such as chip layout optimization, where algorithms can explore vast design spaces to identify configurations that maximize performance and minimize power consumption. Furthermore, machine learning models are being trained on extensive datasets of existing designs to predict the behavior of new circuits, significantly reducing the need for costly and time-intensive physical prototyping and testing. This acceleration of the design cycle isn’t limited to optimization; AI is also being employed in the early stages of design exploration, generating novel architectures and suggesting innovative solutions to complex engineering problems. The result is a pathway toward faster innovation, reduced development costs, and the creation of more efficient and powerful hardware.

Agentic Systems: Deconstructing Complexity Through Algorithmic Decomposition

Agentic AI frameworks address the complexity of hardware design by breaking down overarching tasks into a series of discrete, manageable subtasks. This decomposition allows for parallel processing and distribution of workload, significantly enhancing both computational efficiency and scalability. Instead of attempting to solve the entire design problem at once, the framework assigns individual subtasks – such as logic synthesis, placement, routing, and verification – to specialized agents or modules. The results of each subtask are then integrated, and the process is iterated upon to refine the overall design. This modular approach reduces the computational burden on any single resource and enables the framework to handle designs of increasing complexity without proportional increases in processing time or cost.

Effective agentic systems necessitate a deliberate process of task decomposition, breaking down complex objectives into smaller, more manageable subtasks. This decomposition is coupled with the strategic implementation of Structured Guidance, which provides agents with pre-defined constraints, objectives, and evaluation metrics for each subtask. This guidance isn’t simply instruction; it’s a framework defining permissible actions, data formats, and success criteria. Without such structure, agents can exhibit unpredictable behavior or pursue irrelevant solution paths. The quality of this structured guidance directly impacts the system’s ability to maintain coherence, avoid redundant computations, and ultimately achieve the desired outcome. Proper guidance ensures agents operate within defined boundaries, fostering reliable and predictable performance even with the inherent stochasticity of large language models.

The integration of Small Language Models (SLMs) into agentic AI frameworks addresses the computational demands often associated with large language models while maintaining acceptable performance levels. SLMs, possessing fewer parameters than their larger counterparts, require significantly less processing power and memory, reducing infrastructure costs and enabling deployment on resource-constrained devices. This accessibility extends the potential application of agentic AI to a broader range of users and hardware configurations. While SLMs may exhibit slightly reduced capabilities in complex reasoning compared to larger models, strategic task decomposition within the agentic framework mitigates these limitations, allowing SLMs to effectively manage and execute specific subtasks with sufficient accuracy and efficiency.

Iterative refinement within agentic AI systems involves a cyclical process of solution generation, evaluation, and modification. The system initially produces a solution to a given task, which is then assessed against predefined criteria or through external feedback – potentially including human review. Discrepancies between the current solution and the desired outcome are identified, and this information is used to guide subsequent iterations. Each cycle leverages the results of the previous one, progressively refining the solution toward improved performance or accuracy. This process is crucial for handling complex tasks where a single attempt is unlikely to yield an optimal result and allows the system to adapt to unforeseen challenges or nuanced requirements without requiring explicit reprogramming for each scenario.

This agentic AI framework iteratively refines RTL implementations by retrieving context, constructing SLM-aware prompts, generating candidate designs, validating functionality, and categorizing errors to produce structured refinement feedback.

An Algorithmic Blueprint: Agent Specialization in Hardware Design

The Planning and Pre-processing Agent serves as the initial component of the proposed framework, responsible for establishing a comprehensive understanding of the hardware design task at hand. This agent analyzes high-level specifications and constraints to define the task context, encompassing objectives, required functionalities, and performance metrics. Subsequently, it generates a detailed plan of action, outlining the sequential steps necessary to achieve the desired outcome. This plan includes decomposition of the overall task into manageable sub-tasks, identification of necessary resources, and prioritization of actions to optimize efficiency and ensure successful completion of the hardware design process. The output of this agent is a structured plan that guides the subsequent agents in the framework.

The SLM-aware Prompt Engineering Agent is designed to optimize prompts for use with smaller language models (SLMs), acknowledging their limited context windows and computational resources. This agent utilizes techniques such as prompt compression, example selection, and constraint specification to maximize code generation efficiency and minimize the likelihood of errors. Rather than directly translating high-level task descriptions into prompts, the agent decomposes complex requirements into a series of simpler, SLM-compatible instructions. This process includes identifying key parameters, specifying input/output formats, and providing relevant examples to guide the SLM’s code generation process, ultimately improving both the speed and quality of the resulting code.

The CodeGen Agent is responsible for translating the processed plan and prompts into functional hardware description language (HDL) code, such as Verilog or VHDL. Following code generation, the Validation Agent employs a suite of techniques to verify both the syntax and semantics of the generated code. This includes static analysis for adherence to coding standards and simulation using established hardware verification methodologies to confirm functional correctness against the specified requirements. The Validation Agent outputs pass/fail results and detailed error reports, providing specific feedback on identified issues within the generated code for subsequent refinement.

The Adaptive Feedback Agent operates by analyzing the results returned by the Validation Agent, identifying discrepancies between expected and actual behavior in the generated code. This analysis informs a refinement process where the agent modifies the original code based on the specific failure modes detected during validation. These modifications are not random; the agent utilizes the validation results to pinpoint areas for improvement, such as correcting logical errors, adjusting parameter values, or optimizing code structure. This iterative cycle of validation and refinement continues until the generated code meets the defined success criteria, effectively driving continuous improvement in both code quality and functional correctness. The agent’s capacity to learn from validation failures is crucial for adapting to complex design tasks and achieving robust, reliable hardware implementations.

Empirical Validation: Benchmarking with CVDP

The evaluation of our agentic framework utilized the Comprehensive Verification and Design Platform (CVDP) Benchmark, a standardized suite specifically constructed for assessing capabilities in hardware design automation. CVDP provides a rigorous testing environment with a diverse set of challenges covering various aspects of hardware verification and synthesis, allowing for quantitative comparison against established methodologies and alternative approaches. The benchmark’s comprehensive nature facilitates detailed analysis of our framework’s performance across multiple hardware design tasks, ensuring a robust and objective assessment of its effectiveness and scalability in complex design scenarios.

Evaluations using the CVDP Benchmark demonstrate the competitive performance of the agentic framework relative to traditional hardware design methodologies, with particular gains in efficiency and scalability. Specifically, the DeepSeek-R1 model, when integrated with the framework, achieved a Pass@1 rate of 51.25% on the cid007 code generation task. This result represents a quantifiable improvement over the 44.74% Pass@1 rate achieved by GPT-o4-mini on the same benchmark, indicating enhanced code generation capabilities through the proposed framework.

Evaluation using Small Language Models (SLMs) – specifically DeepSeek-7B, DeepSeek-R1, Granite-4, and Phi-3.5-mini-instruct – demonstrated the framework’s capacity to achieve accuracy levels comparable to those of larger language models in code comprehension tasks. Across benchmarks cid009 and cid010, these SLMs, when integrated with the framework, consistently achieved accuracy rates ranging from 82% to 92%. This indicates that the framework effectively mitigates the performance limitations typically associated with smaller model sizes in code comprehension, offering a viable pathway to maintain high accuracy with reduced computational resources.

The agentic framework demonstrated a significant enhancement in test-passing rates for Small Language Models (SLMs) when performing code-related tasks. Specifically, utilizing the framework resulted in approximately a 100% relative improvement in the number of tests passed by several SLMs-including DeepSeek-7B, DeepSeek-R1, Granite-4, and Phi-3.5-mini-instruct-compared to a single-shot generation approach. This improvement is attributed to the framework’s combined capabilities in both code generation and code comprehension, allowing SLMs to more effectively address complex coding challenges and produce functional outputs.

Towards Sustainable and Automated Hardware Design

The semiconductor industry, vital to modern technology, faces increasing pressure to minimize its environmental footprint. A promising avenue towards this goal lies in the integration of agentic artificial intelligence and streamlined small language models into the hardware design process. This innovative approach moves beyond traditional automation by enabling AI agents to autonomously navigate complex design tasks, iteratively refining solutions and optimizing for resource efficiency. By automating repetitive processes and accelerating design cycles, this framework drastically reduces the energy and material waste typically associated with prototyping and manufacturing. Furthermore, the use of smaller language models, specifically tuned for hardware description and verification, minimizes computational demands, contributing to a more sustainable AI-driven design workflow. This shift not only promises economic benefits through reduced costs but also represents a significant step towards a greener and more responsible future for the technology sector.

The semiconductor industry, vital to modern technology, carries a substantial environmental footprint due to the intensive resource demands and complex manufacturing processes involved in chip design. However, recent advancements in automated design, powered by agentic AI and small language models, offer a promising route towards sustainability. By automating traditionally manual and iterative tasks – such as layout optimization and verification – design cycles are dramatically shortened, reducing energy consumption and material waste. Furthermore, these intelligent systems excel at resource utilization, identifying and implementing designs that minimize silicon area, power draw, and the need for costly and environmentally impactful fabrication steps. This optimization extends beyond mere efficiency; it allows for the exploration of innovative architectures and materials with lower environmental profiles, ultimately contributing to a significantly reduced ecological impact for the entire semiconductor lifecycle.

Current research endeavors are directed toward significantly broadening the scope of this automated design framework, moving beyond initial successes to encompass the intricacies of increasingly complex hardware architectures. This expansion necessitates not only algorithmic advancements to manage greater design space but also a concurrent investigation into the potential of highly compressed language models. The aim is to achieve comparable, or even enhanced, performance with substantially reduced computational demands, fostering greater efficiency and accessibility. Such developments promise a future where automated hardware design is not only sustainable but also readily deployable across a wider range of applications and resource constraints, potentially revolutionizing the field of engineering beyond semiconductors.

The demonstrated success of Large Language Model (LLM)-Based Design Automation extends far beyond the realm of hardware engineering, signaling a potential revolution across numerous disciplines. This paradigm shift-leveraging the power of AI to automate complex design tasks-promises to reshape how engineers approach problems in fields like aerospace, civil engineering, and even mechanical systems design. By streamlining processes, reducing reliance on manual iteration, and enabling rapid prototyping, LLM-based automation offers the potential to drastically accelerate innovation cycles and optimize resource allocation in any field requiring intricate design and analysis. The core principles of translating design specifications into executable parameters, initially proven in semiconductor design, are fundamentally applicable wherever complex systems are conceived and built, suggesting a future where AI acts as an intelligent co-creator in virtually all engineering endeavors.

The pursuit of efficient algorithms, as demonstrated in this exploration of small language models for hardware design, echoes a fundamental tenet of computational thinking. John von Neumann famously stated, “It is possible to carry out any operation which can be carried out by a machine.” This principle underpins the research, suggesting that intelligent task decomposition – a core element of the agentic framework detailed in the article – allows these smaller models to approach the capabilities of their larger counterparts. The focus isn’t merely on achieving a functional result, but on achieving it with minimal resource expenditure, aligning with the paper’s emphasis on energy efficiency and sustainable AI-assisted workflows. The scalability demonstrated through the CVDP benchmark further reinforces this notion of elegant, mathematically grounded solutions.

What’s Next?

The demonstration that reasonably competent hardware design can emerge from constrained language models, orchestrated by agentic frameworks, is… intriguing. However, it skirts the central issue. Performance comparability is not the same as mathematical equivalence. The larger models, while profligate in their resource demands, likely possess an implicit, if unproven, completeness. The smaller models, even when cleverly directed, operate within a defined, and therefore limited, solution space. Establishing formal guarantees – proving, rather than merely observing – remains the crucial, and largely untouched, problem.

Future work must address the limitations inherent in relying on empirical validation. The CVDP benchmark, while useful, represents a constrained subset of the broader hardware design landscape. A more rigorous approach demands the development of formal methods for verifying the correctness and optimality of designs generated by these agentic systems. Simply showing that a circuit functions is insufficient; one must demonstrate, with mathematical certainty, that it is the most efficient, or at least a demonstrably acceptable, solution.

The pursuit of energy efficiency in AI is laudable, but not if achieved at the expense of mathematical rigor. The field should not settle for “good enough” solutions derived from statistical likelihoods. The true elegance lies not in mimicking intelligence, but in replicating the unassailable logic of formal proof.

Original article: https://arxiv.org/pdf/2512.05073.pdf

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