Optimizing Electronic Design Automation (EDA) tool parameters with only dozens of affordable evaluations represents one of the most challenging problems in today’s EDA flow management, where each experiment costs hours to days yet directly impacts final PPA outcomes. While Bayesian Optimization (BO) naturally fits such sample-constrained scenarios, it models the entire EDA flow as a monolithic formulation, blindly ignoring the sequential structure that each stage in the EDA flow affects the next. In this work, we propose a stage-aware optimization framework that fundamentally rethinks EDA parameter tuning. The proposed stage-aware Gaussian process explicitly models cascading relationships between EDA stages through interconnected GP layers, extracting abundant information from each expensive evaluation. To better meet realistic needs, we further introduce Expected Hypervolume Improvement (EHVI)-Efficiency, a time-aware acquisition function that exploits evaluation runtime estimation and EDA tools’ checkpoint reuse to balance design metrics’ expected improvement against EDA flow’s computational cost. Experiments and ablation studies on 6 designs across 3 process nodes demonstrate the effectiveness of our proposed method.

The growing complexity of modern hardware has created vast design spaces that are difficult to explore efficiently. Current design space exploration (DSE) methods treat designs as flat parameter vectors, failing to leverage the rich structural information inherent in hardware architectures. This paper presents a novel RTL hierarchy aware approach to microarchitecture DSE that exploits the natural structure of hardware designs. We propose an RTL hierarchy aware kernel that enables direct comparison of RTL hierarchies, preserving their structural characteristics. Our method incorporates module importance derived from hierarchical synthesis reports through a weighted kernel extension. Additionally, we introduce a clustering method that leverages the proposed kernel to identify distinct architectural patterns, enabling efficient parallel evaluation. Experimental results and ablation studies on a Gemmini-based RISC-V SoC demonstrate the superiority of our approach.

The increasing complexity of modern SoC designs creates challenges in efficiently exploring vast design spaces. Current approaches often reduce microarchitectures to simple parameter vectors, overlooking their rich information embedded in both functional behaviors and implementation details. This paper proposes an LLM-augmented multi-modal fusion method that captures this dual nature of microarchitecture design. By recombining design parameters with their natural language descriptors, we leverage a domain-knowledge-enhanced LLM to extract semantic features that represent functional behavior. Simultaneously, we process Chisel-compiled RTL through a graph neural network to capture structural implementation details. This multi-modal approach enables more effective feature extraction from limited evaluation data. We integrate these rich features into an MLP enhanced with Monte Carlo dropout. This approach provides uncertainty quantification while enabling end-to-end training, allowing the pre-trained feature extractors to be fine-tuned during exploration through Bayesian optimization. Experimental results and ablation studies on a Gemmini-based RISC-V SoC demonstrate that our approach significantly improves exploration efficiency and prediction quality under data limitations.

AI chip scales expediently in the large language models (LLMs) era. In contrast, the existing chip design space exploration (DSE) methods, aimed at discovering optimal yet often infeasible or un-produceable Pareto-front designs, are hindered by neglect of design specifications. In this paper, we propose a novel Spec-driven transformed Bayesian optimization framework to find expected optimal RISC-V SoC architecture designs for LLM tasks. The highlights of our framework lie in a tailored transformed Gaussian process (GP) model prioritizing specified target metrics and a customized acquisition function (EHRM) in multi-objective optimization. Extensive experiments on large-scale RISC-V SoC architecture design explorations for LLMs, such as Transformer, BERT, and GPT-1, demonstrate that our method not only can effectively find the design according to QoR values from the spec, but also outperforms 34.59% in ADRS over state-of-the-art approach with only 66.67% runtime overhead.

Improving the outcomes of very-large-scale integration design without altering the underlying design enablement, such as process, device, interconnect, and IPs, is critical for integrated circuit (IC) designers. Parameter tuning for electronic design automation (EDA) tools is an emerging technology for improving the final design Quality-of-Result (QoR). However, many complex heuristics have been accreted upon previous complex heuristics integrated into tools, resulting in a vast number of tunable parameters. Even worse, these parameters include both continuous and discrete ones, making the parameter tuning process laborious and challenging. In this paper, we propose an attention-based EDA tool parameter explorer. A self-attention mechanism is developed to navigate the parameter importance. A hybrid space Gaussian process model is leveraged to optimize continuous and discrete parameters jointly, capturing their complex interactions. In addition, considering multiple QoR metrics and the large amount of time required to invoke EDA tools, a customized acquisition function based on expected hypervolume improvement (EHVI) is proposed to enable multi-objective optimization and parallel evaluation. Experimental results on a set of IWLS2005 benchmarks demonstrate the effectiveness and efficiency of our method.

In the tide of explosive development in artificial intelligence (AI), the design of AI System-on-Chips (SoCs) is an urgently pressing issue that needs to be addressed. The application of Design Space Exploration (DSE) methods is paramount in pursuing a sound microarchitecture design and improving the quality of results. However, the high-dimensional design parameters and huge design space, which normally occur in the complicated SoCs for Large Language Model (LLM) tasks, pose a great challenge to existing techniques. In this paper, a novel and explainable Bayesian optimization-based framework MCT-Explorer is proposed. A Monte Carlo Tree Search (MCTS)-based method is utilized to analyze the importance of design parameters, guide the sampling directions, mitigate low-quality performance modeling issues, and further improve optimization efficiency. Besides, an information-guided multi-objective optimization function is adopted to balance the multiple metrics (e.g., Cycle. Area, and Power) for SoC design. Our approach can provide guiding opinions and deeper insights for parameter optimization, thus transcending previous arts and achieving an explainable model. Experiment results demonstrate the extraordinary performance of our framework in various high-dimensional (up to hundreds of parameters) and complicated LLM SoC designs.