CPA-DA: A Statistical Regression Framework for Large-Scale Modeling and Optimization of Mixed-Signal Nano Circuits

Carnegie Mellon University, Pittsburgh PA

Investigators

Abstract

Project ID: 0811023 Title: A Statistical Regression Framework for Large-Scale Modeling and Optimization of Mixed-Signal Nano Circuits PI: Xin Li Instt : Carnegie Mellon University ABSTRACT The proposed project aims to automatically synthesize robust high-performance circuits built upon unreliable nano devices and, eventually, to facilitate a bold move from silicon-chip towards nano-chip. To this end, the PI proposes to develop an efficient statistical regression (STAR) framework that facilitates large-scale modeling and optimization of integrated circuits containing 104~105 variables. STAR is derived from a new augmented maximum likelihood estimation (AMLE) scheme. Compared to the traditional least-squares fitting, STAR is expected to achieve 100~1000?e runtime speedup, thereby offering a fundamental infrastructure that enables large-scale statistical modeling and optimization. Both the theoretical aspects (e.g., optimality, convergence, etc.) and the practical implementations (e.g., fast solver, numerical stability, etc.) of STAR will be studied. In addition, the proposed framework will be applied to explore the trade-offs between performance, yield and cost for multiple circuit topologies and device structures of nano electronics. The proposed project aims to initialize a paradigm shift in today¡¦s large-scale mixed-signal design and is expected to yield 2~5x performance improvement for advanced electrical circuits in a broad range of applications, from consumer electronics to aerospace controllers. It would also enable the circuit design community to efficiently and accurately explore the performance trade-offs of nano circuits built upon new devices (e.g., carbon nanotube) and, hence, provides the valuable information to guide future nano electronics research. For this reason, the proposed project would have an immediate impact on today¡¦s semiconductor industry and play an important role to enable American¡¦s leading position. Furthermore, the proposed statistical regression addresses a unique mathematical problem, and it would have fundamental impacts on applied statistics and machine learning. Finally, given the broad coverage over multiple science and engineering fields such as statistical learning, nonlinear optimization, nano electronics, etc., the proposed project offers an excellent opportunity to train the young generation of American researchers. It would substantially improve the American competitiveness by generating high-quality scientists and engineers in multiple areas.