May 22, 2012
703 Northwest Corner
Speaker: Prof. Xin Li (Dept. of Electrical and Computer Engineering, Carnegie Mellon University)
This talk presents several novel methodologies to facilitate large-scale modeling for complex systems. Our work is motivated by the emerging need of large-scale statistical performance modeling for analog and mixed-signal integrated circuits (ICs). The objective is to capture the impact of process and environmental variations for today's nanoscale circuits. In particular, we explore a number of novel statistical techniques (e.g., sparse regression, model fusion, etc) to address the modeling challenges posed by high dimensionality and strong nonlinearity. As such, the parametric yield of nanoscale ICs can be predicted both accurately and efficiently. This talk also discusses how the proposed modeling techniques can be further applied to adaptive post-silicon tuning of analog and mixed-signal circuits. In addition, our algorithms originally developed for VLSI CAD problems have been successfully extended to other non-CAD applications. The second part of this talk briefly discusses a clinical application of brain computer interface based on magnetoencephalography (MEG). The objective of BCI is to provide a direct control pathway from brain to external devices. We will show how statistical modeling algorithms can be applied to improve the signal-to-noise ratio of MEG recording.