AI-Powered Agile Analog Circuit Design and Optimization
원문 초록
Artificial intelligence (AI) techniques are transforming analog circuit design by automating device-level tuning and enabling system-level co-optimization. This paper integrates two approaches: (1) AI-assisted transistor sizing using Multi-Objective Bayesian Optimization (MOBO) for direct circuit parameter optimization, demonstrated on a linearly tunable transconductor; and (2) AI-integrated circuit transfer function modeling for system-level optimization in a keyword spotting (KWS) application, demonstrated by optimizing an analog bandpass filter within a machine learning training loop. The combined insights highlight how AI can improve analog performance, reduce design iteration effort, and jointly optimize analog components and application-level metrics.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
LLINBO: Trustworthy LLM-in-the-Loop Bayesian Optimization
Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control
Fast Bayesian Optimization of Function Networks with Partial Evaluations
Advances in Small-Footprint Keyword Spotting: A Comprehensive Review of Efficient Models and Algorithms
GLAP: General contrastive audio-text pretraining across domains and languages
Amortized variational transdimensional inference
토론
이 논문과 연결된 포럼 글이 아직 없습니다.