Bayesian Optimization-Driven Design Space Exploration Method for Coarse-Grained Reconfigurable Cipher Logic Array

JIANG Danping; DAI Zibin; LIU Yanjiang; ZHOU Zhaoxu; SONG Xiaoyu

doi:10.11999/JEIT250624

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JIANG Danping, DAI Zibin, LIU Yanjiang, ZHOU Zhaoxu, SONG Xiaoyu. Bayesian Optimization-Driven Design Space Exploration Method for Coarse-Grained Reconfigurable Cipher Logic Array[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250624

Citation:

JIANG Danping, DAI Zibin, LIU Yanjiang, ZHOU Zhaoxu, SONG Xiaoyu. Bayesian Optimization-Driven Design Space Exploration Method for Coarse-Grained Reconfigurable Cipher Logic Array[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250624

Citation:

JIANG Danping, DAI Zibin, LIU Yanjiang, ZHOU Zhaoxu, SONG Xiaoyu. Bayesian Optimization-Driven Design Space Exploration Method for Coarse-Grained Reconfigurable Cipher Logic Array[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250624

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Bayesian Optimization-Driven Design Space Exploration Method for Coarse-Grained Reconfigurable Cipher Logic Array

doi: 10.11999/JEIT250624 cstr: 32379.14.JEIT250624

Information Engineering University, Zhengzhou 450000, China

Funds: The National Natural Science Foundation of China (62302519)

Received Date: 2025-07-03
Rev Recd Date: 2025-10-21

Available Online: 2025-10-24

Abstract

Abstract

Objective Coarse-Grained Reconfigurable Cipher Logic Arrays (CGRCAs) are widely employed in information security systems owing to their high flexibility, strong performance, and inherent security. Design Space Exploration (DSE) plays a critical role in evaluating and optimizing the performance of cryptographic algorithms deployed on CGRCAs. However, conventional DSE approaches require extensive computation time to locate optimal solutions in multi-objective optimization problems and often yield suboptimal performance. To overcome these limitations, this study proposes a Bayesian optimization-based DSE framework, termed Multi-Objective Bayesian Optimization-based Exploration (MOBE), which enhances search efficiency and solution quality while effectively satisfying the complex design requirements of CGRCA architectures. Methods The high-dimensional characteristics and multi-objective optimization features of the CGRCA are analyzed, and its design space is systematically modeled. A DSE method based on Bayesian optimization is then proposed, comprising initial sampling design, rapid evaluation model construction, surrogate model development, and acquisition function optimization. A knowledge-aware unsupervised learning sampling strategy is introduced to integrate domain-specific knowledge with clustering algorithms, thereby improving the representativeness and diversity of the initial samples. A rapid evaluation model is established to estimate throughput, area overhead, and Function Unit (FU) utilization for each sample, effectively reducing the computational cost of performance evaluation. To enhance both search efficiency and generalizability, a greedy-based hybrid surrogate model is constructed by combining Gaussian Process with Deep Kernel Learning (DKL-GP), random forest, and neural network models. Moreover, an adaptive multi-acquisition function is designed by integrating Expected Hyper Volume Improvement (EHVI) and quasi-Monte Carlo Upper Confidence Bound (qUCB) to identify the most promising samples and maintain a balanced trade-off between exploration and exploitation. The weighting ratio between EHVI and qUCB is dynamically adjusted to accommodate the varying optimization requirements across different search phases. Results and Discussions The DSE method based on Bayesian optimization (Algorithm 2) includes initial sampling design, rapid evaluation model construction, surrogate model development, and acquisition function optimization to enhance solution quality and search efficiency. Simulation results show that the knowledge-aware unsupervised learning sampling strategy reduces the Average Distance from Reference Set (ADRS) by up to 28.2% and increases hypervolume by 15.1% compared with existing sampling approaches (Table 3). This improvement primarily arises from the integration of domain knowledge with clustering algorithms. Compared with single surrogate model–based DSE methods, the greedy-based hybrid surrogate model leverages the complementary advantages of multiple surrogate models across different optimization stages, prioritizing samples that contribute most to hypervolume expansion. The hybrid surrogate model achieves a reduction in ADRS of up to 31.7% and an improvement in hypervolume of 20.0% (Table 4). Furthermore, the proposed MOBE framework achieves a 34.9% reduction in ADRS and increases hypervolume by 28.7% relative to state-of-the-art DSE methods (Table 5). Regarding the average performance metrics of Pareto-front samples, MOBE enhances throughput by up to 29.9%, reduces area overhead by 6.0%, and improves FU utilization by 11.6% (Fig. 6), confirming its superiority in overall solution quality. Moreover, the MOBE method exhibits excellent cross-algorithm stability in both hypervolume and Normalized Overall Execution Time (NOET) (Fig. 7 and Table 6). Conclusions This study presents a multi-objective DSE method based on Bayesian optimization that enhances both solution quality and search efficiency for CGRCA. The proposed approach employs a knowledge-aware unsupervised learning sampling strategy to generate an initial sample set with high representativeness and diversity. A rapid evaluation model is subsequently developed to reduce the computational cost of performance assessments. Additionally, the integration of adaptive multi-acquisition functions with a greedy-based hybrid surrogate model further improves the efficiency and generalization capability of the DSE framework. Comparative experiments demonstrate the effectiveness of the proposed MOBE method: (1) the sampling strategy reduces the ADRS by up to 28.2% and increases hypervolume by 15.1% compared with existing methods; (2) the greedy-based hybrid surrogate model achieves up to a 31.7% reduction in ADRS and a 20.0% improvement in hypervolume relative to single surrogate model–based approaches; (3) the overall MOBE framework achieves a 34.9% reduction in ADRS and a 28.7% increase in hypervolume compared with state-of-the-art DSE techniques; (4) MOBE improves throughput by up to 29.9%, reduces area overhead by 6.0%, and increases FU utilization by 11.6% relative to existing methods; and (5) MOBE exhibits excellent cross-algorithm stability in hypervolume and NOET. MOBE is applicable to medium-and-high-performance cryptographic application scenarios, including cloud platforms and desktop terminals. Nevertheless, two limitations remain. First, MOBE currently employs only traditional surrogate models, which may constrain feature learning efficiency and modeling accuracy. Second, its validation is confined to a CGRCA architecture previously developed by the research group, lacking verification across existing CGRCA architectures. Future work will address these limitations by incorporating emerging artificial intelligence techniques, such as large models, and conducting extensive experiments on diverse CGRCA architectures to further enhance the generalization and effectiveness of MOBE.
- Coarse-Grained Reconfigurable Cipher Logic Array (CGRCA),
- Design Space Exploration (DSE),
- Bayesian optimization,
- Random forest,
- Neural network

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References(21)

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