EDA Technology in Digital Circuit Design: A Study on Application Methodologies
Keywords:
Digital circuit design, EDA technology, FPGA, Automated designAbstract
Digital circuit design is a crucial foundational element of modern electronic engineering, and the application of EDA technology provides efficient, automated development methods, demonstrating significant advantages especially in FPGA design. This paper focuses on the practical application of EDA technology in digital circuit design, emphasizing optimization strategies such as standardizing design languages, strengthening timing control, streamlining resource structures, and refining simulation mechanisms. By integrating specific design cases, it analyzes the supporting role of EDA tools in modeling, synthesis, placement, and verification, promoting more efficient and reliable FPGA circuit development and comprehensively enhancing digital system performance.
References
Peng, Q., Planche, B., Gao, Z., Zheng, M., Choudhuri, A., Chen, T., Chen, C. and Wu, Z., 3D Vision-Language Gaussian Splatting. In The Thirteenth International Conference on Learning Representations.
Guo, Y. (2025). The Optimal Trajectory Control Using Deterministic Artifi cial Intelligence for Robotic Manipulator. Industrial Technology Research, 2(3).
Zhou, Z. (2025). Research on Software Performance Monitoring and Optimization Strategies in Microservices Architecture. Artificial Intelligence Technology Research, 2(9).
Wei, Xiangang, et al. "AI driven intelligent health management systems in telemedicine: An applied research study." Journal of Computer Science and Frontier Technologies 1.2 (2025): 78-86.
We, X., Lin, S., Pruś, K., Zhu, X., Jia, X., & Du, R. (2025). Towards Intelligent Monitoring of Anesthesia Depth by Leveraging Multimodal Physiological Data. International Journal of Advance in Clinical Science Research, 4, 26–37. Retrieved from https://www.h-tsp.com/index.php/ijacsr/article/view/158
Zhang, Wenqing, et al. "Enhancing Logical Reasoning in Large Language Models via Multi-Stage Ensemble Architecture with Adaptive Attention and Decision Voting." Proceedings of the 2024 5th International Conference on Big Data Economy and Information Management. 2024.
Zhang, W., Shih, K., Jin, Y., Chen, Z., Liu, L., & Zhang, Z. (2025, January). Dynamic Cross-Attention and Multi-Level Feature Fusion for Fine-Grained Image Captioning in Advertising. In 2025 5th International Conference on Neural Networks, Information and Communication Engineering (NNICE) (pp. 282-286). IEEE.
Zhang, D., Fu, J., Zheng, J., Deng, Z., & Yang, Z. (2025). Maximizing Scoring Divergence in Automated Essay Assessment with LLaMA-Based Meta-Attention Networks.
Huang, X., Lin, Z., Sun, F., Zhang, W., Tong, K., & Liu, Y. (2025). Enhancing Document-Level Question Answering via Multi-Hop Retrieval-Augmented Generation with LLaMA 3. arXiv preprint arXiv:2506.16037.
Wang, Y., & Bi, X. (2025, January). Hierarchical Adaptive Fine-Tuning Framework for Enhancing Multi-Task Learning in Large-Scale Models. In 2025 5th International Conference on Neural Networks, Information and Communication Engineering (NNICE) (pp. 1582-1586). IEEE.
Liu, C. (2025, January). Optimization of Adaboost cardiac disease prediction and classification based on long and short term memory network. In 5th International Conference on Signal Processing and Machine Learning (CONF SPML 2025) (Vol. 2025, pp. 196-200). IET.
Su, Z., Yang, D., Wang, C., Xiao, Z., & Cai, S. (2025). Structural assessment of family and educational influences on student health behaviours: Insights from a public health perspective. Plos one, 20(9), e0333086.
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