A sub-threshold cell library and methodology

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (p. 97-102).Sub-threshold operation is a compelling approach for energy-constrained applications where speed is of secondary concern, but increased sensitivity to process variation must be mitigated in this regime. With scaling of process technologies, random within-die variation has recently introduced another degree of complexity in circuit design. This thesis proposes approaches to mitigate process variation in sub-threshold circuits through device sizing, topology selection and fault-tolerant architecture. This thesis makes several contributions to a sub-threshold circuit design methodology. A formal analysis of device sizing trade-offs between delay, energy, and variability reveals that while minimum size devices provide lowest energy and delay in sub-threshold, their increased sensitivity to random dopant fluctuation may cause functional errors. A proposed variation-driven design approach enables consistent sizing of logic gates and registers for constant functional yield. A yield constraint imposes energy overhead at low power supply voltages and changes the minimum energy operating point of a circuit.(cont.) The optimal supply and device sizing depend on the topology of the circuit and its energy versus VDD characteristic. The analysis resulted in a 56-cell library in 65nm CMOS, which is incorporated in a computer-aided design flow. A test chip synthesized from this library implements a fault-tolerant FIR filter. Algorithmic error detection enables correction of transient timing errors due to delay variability in sub-threshold, and also allows the system frequency to be set more aggressively for the average case instead of the worst case.by Joyce Y.S. Kwong.S.M

Robust model-based fault diganosis [sic] for a DC zonal electrical distribution system

A key element of the U.S. Navy's transition to an electric naval force is an Integrated Power System (IPS) that provides continuity of service to vital systems despite combat damage. In order to meet subsequent survivability standards under a reduced manning constraint, the IPS system must include a fault tolerant control scheme, capable of achieving automated graceful degradation despite major disruptions involving cascading failures. Toward this objective, online modelbased residual generation techniques are proposed, which identify explicitly defined faults within a stochastic DC Zonal Electrical Distribution System (DC ZEDS). Two novel polynomial approaches to the design of unknown input observers (UIO) are developed to estimate the partial state and, under certain conditions, the unknown input. These methods are shown to apply to a larger class of systems compared to standard projection based approaches where the UIO rank condition is not satisfied. It is shown that the partial-state estimate is sufficient to the computation of residuals for fault diagnosis, even in such cases where full-state estimation is not possible. In order to reduce the complexity of the system, a modular approach to Fault Detection and Isolation (FDI) is presented. Here, the innovations generated from a bank of Kalman filters (some of them UIOs) act as a structured residual set for the stochastic DC ZEDS subsystem modules and are shown to detect and isolate various classes of faults. Certain mathematical models are also shown to effectively identify input/output consistency of systems in explicitly defined fault conditions. Numerical simulation results are based on the well-documented Office of Naval Research Control Challenge benchmark system, which represents a prototypical U.S. Navy shipboard IPS power distribution system.http://archive.org/details/robustmodelbased1094510226Approved for public release; distribution is unlimited

Online Audio-Visual Multi-Source Tracking and Separation: A Labeled Random Finite Set Approach

The dissertation proposes an online solution for separating an unknown and time-varying number of moving sources using audio and visual data. The random finite set framework is used for the modeling and fusion of audio and visual data. This enables an online tracking algorithm to estimate the source positions and identities for each time point. With this information, a set of beamformers can be designed to separate each desired source and suppress the interfering sources