Custom Integrated Circuits

Contains reports on twelve research projects.Analog Devices, Inc.International Business Machines, Inc.Joint Services Electronics Program (Contract DAAL03-86-K-0002)Joint Services Electronics Program (Contract DAAL03-89-C-0001)U.S. Air Force - Office of Scientific Research (Grant AFOSR 86-0164)Rockwell International CorporationOKI Semiconductor, Inc.U.S. Navy - Office of Naval Research (Contract N00014-81-K-0742)Charles Stark Draper LaboratoryNational Science Foundation (Grant MIP 84-07285)National Science Foundation (Grant MIP 87-14969)Battelle LaboratoriesNational Science Foundation (Grant MIP 88-14612)DuPont CorporationDefense Advanced Research Projects Agency/U.S. Navy - Office of Naval Research (Contract N00014-87-K-0825)American Telephone and TelegraphDigital Equipment CorporationNational Science Foundation (Grant MIP-88-58764

Approximate logic circuits: Theory and applications

CMOS technology scaling, the process of shrinking transistor dimensions based on Moore's law, has been the thrust behind increasingly powerful integrated circuits for over half a century. As dimensions are scaled to few tens of nanometers, process and environmental variations can significantly alter transistor characteristics, thus degrading reliability and reducing performance gains in CMOS designs with technology scaling. Although design solutions proposed in recent years to improve reliability of CMOS designs are power-efficient, the performance penalty associated with these solutions further reduces performance gains with technology scaling, and hence these solutions are not well-suited for high-performance designs. This thesis proposes approximate logic circuits as a new logic synthesis paradigm for reliable, high-performance computing systems. Given a specification, an approximate logic circuit is functionally equivalent to the given specification for a "significant" portion of the input space, but has a smaller delay and power as compared to a circuit implementation of the original specification. This contributions of this thesis include (i) a general theory of approximation and efficient algorithms for automated synthesis of approximations for unrestricted random logic circuits, (ii) logic design solutions based on approximate circuits to improve reliability of designs with negligible performance penalty, and (iii) efficient decomposition algorithms based on approxiiii mate circuits to improve performance of designs during logic synthesis. This thesis concludes with other potential applications of approximate circuits and identifies. open problems in logic decomposition and approximate circuit synthesis

Learning understandable classifier models.

The topic of this dissertation is the automation of the process of extracting understandable patterns and rules from data. An unprecedented amount of data is available to anyone with a computer connected to the Internet. The disciplines of Data Mining and Machine Learning have emerged over the last two decades to face this challenge. This has led to the development of many tools and methods. These tools often produce models that make very accurate predictions about previously unseen data. However, models built by the most accurate methods are usually hard to understand or interpret by humans. In consequence, they deliver only decisions, and are short of any explanations. Hence they do not directly lead to the acquisition of new knowledge. This dissertation contributes to bridging the gap between the accurate opaque models and those less accurate but more transparent for humans. This dissertation first defines the problem of learning from data. It surveys the state-of-the-art methods for supervised learning of both understandable and opaque models from data, as well as unsupervised methods that detect features present in the data. It describes popular methods of rule extraction from unintelligible models which rewrite them into an understandable form. Limitations of rule extraction are described. A novel definition of understandability which ties computational complexity and learning is provided to show that rule extraction is an NP-hard problem. Next, a discussion whether one can expect that even an accurate classifier has learned new knowledge. The survey ends with a presentation of two approaches to building of understandable classifiers. On the one hand, understandable models must be able to accurately describe relations in the data. On the other hand, often a description of the output of a system in terms of its input requires the introduction of intermediate concepts, called features. Therefore it is crucial to develop methods that describe the data with understandable features and are able to use those features to present the relation that describes the data. Novel contributions of this thesis follow the survey. Two families of rule extraction algorithms are considered. First, a method that can work with any opaque classifier is introduced. Artificial training patterns are generated in a mathematically sound way and used to train more accurate understandable models. Subsequently, two novel algorithms that require that the opaque model is a Neural Network are presented. They rely on access to the network\u27s weights and biases to induce rules encoded as Decision Diagrams. Finally, the topic of feature extraction is considered. The impact on imposing non-negativity constraints on the weights of a neural network is considered. It is proved that a three layer network with non-negative weights can shatter any given set of points and experiments are conducted to assess the accuracy and interpretability of such networks. Then, a novel path-following algorithm that finds robust sparse encodings of data is presented. In summary, this dissertation contributes to improved understandability of classifiers in several tangible and original ways. It introduces three distinct aspects of achieving this goal: infusion of additional patterns from the underlying pattern distribution into rule learners, the derivation of decision diagrams from neural networks, and achieving sparse coding with neural networks with non-negative weights

A parallel algorithm for multi-level logic synthesis using the transduction method

The Transduction Method has been shown to be a powerful tool in the optimization of multilevel networks. Many tools such as the SYLON synthesis system (X90), (CM89), (LM90) have been developed based on this method. A parallel implementation is presented of SYLON-XTRANS (XM89) on an eight processor Encore Multimax shared memory multiprocessor. It minimizes multilevel networks consisting of simple gates through parallel pruning, gate substitution, gate merging, generalized gate substitution, and gate input reduction. This implementation, called Parallel TRANSduction (PTRANS), also uses partitioning to break large circuits up and performs inter- and intra-partition dynamic load balancing. With this, good speedups and high processor efficiencies are achievable without sacrificing the resulting circuit quality

Gendered bio-responsibilities and travelling egg providers from South Africa

‘Unsuspecting young South African women are heading overseas to donate their eggs to infertile couples and earn a free international holiday in the process. But, at what cost? This was the voice-over during a news show in South Africa in 2016 that described the phenomenon of young white South African women going abroad to donate their eggs. Through the media, medical professionals sought to warn naïve girls about unscrupulous agencie taking advantage of them, and in doing so putting them at grave medical risks in Third Worl clinics. Yet owners of agencies and egg providers themselves countered this imagery; here, the egg provider becomes a far more complex biocitizen who finds an opportunity to combine an act of altruism with an opportunity to earn money and travel. Through interviews with travelling egg providers, doctors and egg agencies, and analysis of public and social media, we analyse these competing discourses critically by situating them within the specific context of egg provision in South Africa. We argue that travelling egg providers' defence of their involvement may challenge some gendered assumptions made by the media and medical staff, but at the same time reaffirm what we call gendered bio-responsibilities or the gendered nature of the emphasis on (individual) responsibilization of biological citizens. By focusing on a relatively understudied aspect of the burgeoning literature on biocitizenship, we argue that the project of biocitizenship assists the expansion and normalization of new biomedical technologies, often without proper emphasis on the disproportionate obligations on the women involved

Advanced Algorithms for VLSI: Statistical Circuit Optimization and Cyclic Circuit Analysis

This work focuses on two emerging fields in VLSI. The first is use of statistical formulations to tackle one of the classical problems in VLSI design and analysis domains, namely gate sizing. The second is on analysis of nontraditional digital systems in the form of cyclic combinational circuits. In the first part, a new approach for enhancing the process-variation tolerance of digital circuits is described. We extend recent advances in statistical timing analysis into an optimization framework. Our objective is to reduce the performance variance of a technology-mapped circuit where delays across elements are represented by random variables which capture the manufacturing variations. We introduce the notion of statistical critical paths, which account for both means and variances of performance variation. An optimization engine is used to size gates with a goal of reducing the timing variance along the statistical critical paths. Circuit optimization is carried out using a gain-based gate sizing algorithm that terminates when constraints are satisfied or no further improvements can be made. We show optimization results that demonstrate an average of 72% reduction in performance variation at the expense of average 20% increase in design area. In the second part, we tackle the problem of analyzing cyclic circuits. Compiling high-level hardware languages can produce circuits containing combinational cycles that can never be sensitized. Such circuits do have well-defined functional behavior, but wreak havoc with most tools, which assume acyclic combinational logic. As such, some sort of cycle-removal step is usually necessary. We present an algorithm able to quickly and exactly characterize all combinational behavior of a cyclic circuit. It used a combination of explicit and implicit methods to compute input patterns that make the circuit behave combinationally. This can be used to restructure the circuit into an acyclic equivalent, report errors, or as an optimization aid. Experiments show our algorithm runs several orders of magnitude faster than existing ones on real-life cyclic circuits, making it useful in practice

The power of implicit social relation in rating prediction of social recommender systems of social recommender

The explosive growth of social networks in recent times has presented a powerful source of information to be utilized as an extra source for assisting in the social recommendation problems. The social recommendation methods that are based on probabilistic matrix factorization improved the recommendation accuracy and partly solved the cold-start and data sparsity problems. However, these methods only exploited the explicit social relations and almost completely ignored the implicit social relations. In this article, we firstly propose an algorithm to extract the implicit relation in the undirected graphs of social networks by exploiting the link prediction techniques. Furthermore, we propose a new probabilistic matrix factorization method to alleviate the data sparsity problem through incorporating explicit friendship and implicit friendship. We evaluate our proposed approach on two real datasets, Last.Fm and Douban. The experimental results show that our method performs much better than the state-of-the-art approaches, which indicates the importance of incorporating implicit social relations in the recommendation process to address the poor prediction accuracy