Ata, S. K., Kong, Z. H., James, A., Cai, L., Yeo, K. S., Mi Mi Aung, K., Foo, C. S., & James, A. (2024, May 19). The Initialization Factor: Understanding its Impact on Active Learning for Analog Circuit Design. 2024 IEEE International Symposium on Circuits and Systems (ISCAS). https://doi.org/10.1109/iscas58744.2024.10558675
Abstract:
Active learning, which aims to enhance modeling efficiency, precision, and cost effectiveness through selective labeling, is emerging as a promising strategy for analog circuit modeling. However, analog circuits are constrained by strict functional and technological limitations, resulting in scarcity of data for modeling, and additional data acquisition involves expensive and time-consuming simulations. For efficient and effective active learning for analog circuit modeling, our research analyzes data-driven initial sampling techniques which lays the foundation for the active learning process. Our experiments reveal that these initialization strategies expedite the learning process, decrease the demand for extensive simulations, and produces more accurate models. Furthermore, the results demonstrate that active learning techniques, which uniformly sample the design space, tend to benefit from distance-based initialization technique.
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Funding Info:
This research / project is supported by the Agency for Science, Technology and Research (A*STAR) - AME Programmatic Funds
Grant Reference no. : A19E3b0099
This research / project is supported by the Agency for Science, Technology and Research (A*STAR) - AME Programmatic Funds
Grant Reference no. : A20H6b0151