Low-power design techniques for high-performance CMOS adders

A high-performance adder is one of the most critical components of a processor which determines its throughput, as it is used in the ALU, the floating-point unit, and for address generation in case of cache or memory access. In this paper, low-power design techniques for various digital circuit families are studied for implementing high-performance adders, with the objective to optimize performance per watt or energy efficiency as well as silicon area efficiency. While the investigation is done using 100 MHz, 32 b carry lookahead (CLA) adders in a 0.6 μm CMOS technology, most techniques presented here can also be applied to other parallel adder algorithms such as carry-select adders (CSA) and other energy efficient CMOS circuits. Among the techniques presented here, the double pass-transistor logic (DPL) is found to be the most energy efficient while the single-rail domino and complementary pass-transistor logic (CPL) result in the best performance and the most area efficient adders, respectively. The impact of transistor threshold voltage scaling on energy efficiency is also examined when the supply voltage is scaled from 3.5 V down to 1.0 V     

Talk in virtual contexts: reflecting on participation and online learning models

Computer-mediated conferencing has been adopted, particularly for purposes of online course provision, as a method that can deliver community. Widespread interest in a communities-of-practice approach within both informal and formal learning has strengthened perceptions of the value of creating a community online. A case study of asynchronous computer conferencing for the purposes of leadership development in schools is the focus for a study of the features and the discursive quality of the online interaction that occurred. Two analytical approaches are used: discourse analysis and social network analysis. These highlighted different aspects of the case, in terms of the role of peers versus the expert moderator (or 'hotseat guest'), the extent of readership versus contribution and the tone and content of the discussion. Evidence that contributors were learning from the interaction was identified and strong links with place-based communities of practice were also evident. Nevertheless, the online interaction could not be said to constitute a community or to be evidently developing in that direction. Its features as a network, where weak links were key to a sharing and knowledge-construction process, were more salient. Networks offer the potential for weak connections that have a particular value for connecting across dispersed practitioners and potentially bridging between communities of practice and other forms of organisation and groupings.     

Using NNF to transform conceptual data models to object-oriented database designs

More work is needed on devising practical, but theoretically well-founded procedures for doing object-oriented database (OODB) design [17]. Design procedures should also be flexible enough to take into account various application characteristics (such as whether objects are very large or are read-only). In this paper, we present and discuss an OODB design procedure that addresses these problems. The procedure is practical in the sense that it is based on a common family of conceptual models and in the sense that it does not expect users to supply esoteric, difficult-to-discover, and hard-to-understand constraints (such as multivalued dependencies), nor does it make hard-to-check and easy-to-overlook assumptions (such as the universal relation scheme assumption). At the same time, the procedure is well-founded and formal, being based on NNF (Nested Normal Form [21]), a new theoretical result that characterizes properties of interest in designing complex objects. It is also adaptable to various applications characteristics.     

A learning algorithm for Recurrent Radial Basis Function Networks

This paper proposes a Recurrent Radial Basis Function network (RRBFN) that can be applied to temporal pattern classifications and predictions. Based on the architecture of the conventional Radial Basis Function networks, the RRBFNs use Gaussian nonlinearity and have feedback paths between every hidden node. These feedback paths enable the networks to learn temporal patterns without an input buffer to hold the recent elements of an input sequence. A gradient descent learning algorithm for the RRBFNs is derived. Two RRBFNs with different number of hidden nodes were tested using a temporal sequence generated by an infinite impulse response filter. The results show that the RRBFNs can approximate the filter more accurate than the Continually Running Fully Recurrent networks trained by the Real-Time Recurrent Learning algorithm.     

Electrophoretic Deposition (EPD): Mechanisms, Kinetics, and Application to Ceramics

The mechanisms of electrophoretic deposition (EPD) are discussed and their shortcomings identified. The kinetics of the processes involved are analyzed for constant-current and constant-voltage conditions. A method of determining the Hamaker constant of suspended particles is developed by modeling the relationship between the particle inter-action energy and the suspension stability. A three-probe dc technique is used to map the voltage profile around the depositing electrode, and the results are used to explain discrepancies between the calculated and experimentally observed voltage drops during deposition. A mechanism of deposition is proposed based on DLVO theory and particle double-layer distortion/thinning on application of a dc field to the suspension. Kinetic equations are developed for constant-current and constant-voltage EPD using mass balance conditions; these are verified by experiments. After the phenomenon is introduced and discussed, a critique of the application of EPD to the synthesis of ceramic shapes and coatings is given.