SAT sweeping with local observability don't-cares
SAT sweeping is a method for simplifying an AND/inverter graph (AIG) by systematically merging graph vertices from the inputs towards the outputs using a combination of structural hashing, simulation, and SAT queries. Due to its robustness and efficiency, SAT sweeping provides a solid algorithm for Boolean reasoning in functional verification and logic synthesis. In previous work, SAT sweeping merges two vertices only if they are functionally equivalent. In this paper we present a significant extension of the SAT-sweeping algorithm that exploits local observability don't-cares (ODCs) to increase the number of vertices merged. We use a novel technique to bound the use of ODCs and thus the computational effort to find them, while still finding a large fraction of them. Our reported results based on a set of industrial benchmark circuits demonstrate that ODC-based SAT sweeping results in significantly more graph simplification with great benefit for Boolean reasoning with a moderate increase in computational effort

title: Period Optimization for Hard Real-time Distributed Automotive Systems
abstract: The complexity and physical distribution of modern active-safety automotive applications requires the use of distributed architectures. These architectures consist of multiple electronic control units (ECUs) connected with standardized buses. The most common configuration features periodic activation of tasks and messages coupled with run-time priority-based scheduling. The correct deployment of applications on such architectures requires end-to- end latency deadlines to be met. This is challenging since deadlines must be enforced across a set of ECUs and buses, each of which supports multiple functionality. The need for accommodating legacy tasks and messages further complicates the scenario. In this work, we automatically assign task and message periods for distributed automotive systems. This is accomplished by leveraging schedulability analysis within a convex optimization framework to simultaneously assign periods and satisfy end-to-end latency constraints. Our approach is applied to an industrial case study as well as an example taken from the literature and is shown to be both effective and efficient.

title: JPEG encoding on the Intel MXP5800: a platform-based design case study
abstract: Multimedia systems are becoming increasingly complex and concurrent. The platform-based design (PBD) methodology (Keutzer et al., 2000) tackles these issues by recommending the use of formal models, carefully defined abstraction layers and the separation of concerns. Models of computation (Lee and Sangiovanni-Vincentelli, 1998) (MoCs) can be used within this methodology to enable specialized synthesis and verification techniques. In this paper, these concepts are leveraged in an industrial case study: the JPEG encoder application deployed on the Intel MXP5800 imaging processor. The modeling is carried out in the Metropolis (Balarin et al., 2003) design framework. We show that the system-level model using our chosen model of computation allows performance estimation within 5% of the actual implementation. Moreover, the chosen MoC is amenable to automation, which enables future synthesis techniques.

title: Efficient octilinear steiner tree construction based on spanning graphs
abstract: Octilinear interconnect is a promising technique to shorten wire lengths. We present two practical heuristic octilinear Steiner tree (OSMT) algorithms in the paper. They are both based on octilinear spanning graphs. The one by edge substitution (OST-E) has a worst case running time of O(nlogn) and similar performance as the batched greedy algorithm[9]. The other one by triangle contraction (OST-T) has a small increase in running time and better performance. Experiments on both industry and random test cases are conducted.

title: A semantic-driven synthesis flow for platform-based design
abstract: In this work, we propose a semantics-driven synthesis flow, in which the semantics and the abstraction level are determined formally by using the concept of a common modeling domain between functionality and architecture. By doing so, a formal synthesis procedure can be defined and algorithms for automatic optimal mapping derived

title: Exploiting Similarity of Subqueries for Complex Query Optimization
abstract: Query optimizers in current database management systems (DBMS) often face problems such as intolerably long optimization time and/or poor optimization results when optimizing complex queries. To tackle this challenge, we present a new similarity-based optimization technique in this paper. The key idea is to identify groups of similar subqueries that often appear in a complex query and share the optimization result within each group in the query. An efficient algorithm to identify similar queries in a given query and optimize the query based on similarity is presented. Our experimental results demonstrate that the proposed technique is quite promising in optimizing complex queries in a DBMS.

title: Classifying Local Queries for Global Query Optimization in Multidatabase Systems
abstract: A multidatabase system (MDBS) integrates information from multiple pre-existing local databases. A major challenge for global query optimization in an MDBS is that some required local information a...

title: Tandem Connectionist Feature Extraction for Conversational Speech Recognition
abstract: Multi-Layer Perceptrons (MLPs) can be used in automatic speech recognition in many ways. A particular application of this tool over the last few years has been the Tandem approach, as described in [7] and other more recent publications. Here we discuss the characteristics of the MLP-based features used for the Tandem approach, and conclude with a report on their application to conversational speech recognition. The paper shows that MLP transformations yield variables that have regular distributions, which can be further modified by using logarithm to make the distribution easier to model by a Gaussian-HMM. Two or more vectors of these features can easily be combined without increasing the feature dimension. We also report recognition results that show that MLP features can significantly improve recognition performance for the NIST 2001 Hub-5 evaluation set with models trained on the Switchboard Corpus, even for complex systems incorporating MMIE training and other enhancements.

title: Global Query Optimization Based on Multistate Cost Models for a Dynamic Multidatabase System
abstract: Global query optimization in a multidatabase system (MDBS) is a challenging issue since some local optimization information such as local cost models may not be available at the global level due to local autonomy. It becomes even more difficult when dynamic environmental factors are taken into consideration. In our previous work, a qualitative approach was suggested to build so-called multistate cost models to capture the performance behavior of a dynamic multidatabase environment. It has been shown that a multistate cost model can give a good cost estimate for a query run in any contention state in the dynamic environment. In this paper, we present a technique to perform query optimization based on multistate cost models for a dynamic MDBS. Two relevant algorithms are proposed. The first one selects a set of representative system environmental states for generating an execution plan with multiple versions for a given query at compile time, while the second one efficiently determines the best version to invoke for the query at run time. Experiments demonstrate that the proposed technique is quite promising for performing global query optimization in a dynamic MDBS. Compared with related work on dynamic query optimization, our approach has an advantage of avoiding the high overhead for modifying or re-generating an execution plan for a query based on dynamic run-time information.

title: Long-Term Temporal Features for Conversational Speech Recognition
abstract: The automatic transcription of conversational speech, both from telephone and in-person interactions, is still an extremely challenging task. Our efforts to recognize speech from meetings is likely to benefit from any advances we achieve with conversational telephone speech, a topic of considerable focus for our research. Towards both of these ends, we have developed, in collaboration with our colleagues at SRI and IDIAP, techniques to incorporate long-term (~500 ms) temporal information using multi-layered perceptrons (MLPs). Much of this work is based on prior achievements in recent years at the former lab of Hynek Hermansky at the Oregon Graduate Institute (OGI), where the TempoRAl Pattern (TRAP) approach was developed. The contribution here is to present experiments showing: 1) that simply widening acoustic context by using more frames of full band speech energies as input to the MLP is suboptimal compared to a more constrained two-stage approach that first focuses on long-term temporal patterns in each critical band separately and then combines them, 2) that the best two-stage approach studied utilizes hidden activation values of MLPs trained on the log critical band energies (LCBEs) of 51 consecutive frames, and 3) that combining the best two-stage approach with conventional short-term features significantly reduces word error rates on the 2001 NIST Hub-5 conversational telephone speech (CTS) evaluation set with models trained using the Switchboard Corpus.