E-QED: Electrical Bug Localization During Post-Silicon Validation Enabled by Quick Error Detection and Formal Methods

During post-silicon validation, manufactured integrated circuits are extensively tested in actual system environments to detect design bugs. Bug localization involves identification of a bug trace (a sequence of inputs that activates and detects the bug) and a hardware design block where the bug is located. Existing bug localization practices during post-silicon validation are mostly manual and ad hoc, and, hence, extremely expensive and time consuming. This is particularly true for subtle electrical bugs caused by unexpected interactions between a design and its electrical state. We present E-QED, a new approach that automatically localizes electrical bugs during post-silicon validation. Our results on the OpenSPARC T2, an open-source 500-million-transistor multicore chip design, demonstrate the effectiveness and practicality of E-QED: starting with a failed post-silicon test, in a few hours (9 hours on average) we can automatically narrow the location of the bug to (the fan-in logic cone of) a handful of candidate flip-flops (18 flip-flops on average for a design with ~ 1 Million flip-flops) and also obtain the corresponding bug trace. The area impact of E-QED is ~2.5%. In contrast, deter-mining this same information might take weeks (or even months) of mostly manual work using traditional approaches

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Semantic Support for Log Analysis of Safety-Critical Embedded Systems

Testing is a relevant activity for the development life-cycle of Safety Critical Embedded systems. In particular, much effort is spent for analysis and classification of test logs from SCADA subsystems, especially when failures occur. The human expertise is needful to understand the reasons of failures, for tracing back the errors, as well as to understand which requirements are affected by errors and which ones will be affected by eventual changes in the system design. Semantic techniques and full text search are used to support human experts for the analysis and classification of test logs, in order to speedup and improve the diagnosis phase. Moreover, retrieval of tests and requirements, which can be related to the current failure, is supported in order to allow the discovery of available alternatives and solutions for a better and faster investigation of the problem.Comment: EDCC-2014, BIG4CIP-2014, Embedded systems, testing, semantic discovery, ontology, big dat

Advancing automation and robotics technology for the Space Station Freedom and for the US economy

Described here is the progress made by Levels 1, 2, and 3 of the Space Station Freedom in developing and applying advanced automation and robotics technology. Emphasis was placed on the Space Station Freedom program responses to specific recommendations made in the Advanced Technology Advisory Committee (ATAC) Progress Report 13, and issues of A&R implementation into the payload operations integration Center at Marshall Space Flight Center. Assessments are presented for these and other areas as they apply to the advancement of automation and robotics technology for Space Station Freedom

Automated extraction of knowledge for model-based diagnostics

The concept of accessing computer aided design (CAD) design databases and extracting a process model automatically is investigated as a possible source for the generation of knowledge bases for model-based reasoning systems. The resulting system, referred to as automated knowledge generation (AKG), uses an object-oriented programming structure and constraint techniques as well as internal database of component descriptions to generate a frame-based structure that describes the model. The procedure has been designed to be general enough to be easily coupled to CAD systems that feature a database capable of providing label and connectivity data from the drawn system. The AKG system is capable of defining knowledge bases in formats required by various model-based reasoning tools

Enhancement of COPD Biological Networks Using a Web-Based Collaboration Interface

The construction and application of biological network models is an approach that offers a holistic way to understand biological processes involved in disease. Chronic obstructive pulmonary disease (COPD) is a progressive inflammatory disease of the airways for which therapeutic options currently are limited after diagnosis, even in its earliest stage. COPD network models are important tools to better understand the biological components and processes underlying initial disease development. With the increasing amounts of literature that are now available, crowdsourcing approaches offer new forms of collaboration for researchers to review biological findings, which can be applied to the construction and verification of complex biological networks. We report the construction of 50 biological network models relevant to lung biology and early COPD using an integrative systems biology and collaborative crowd-verification approach. By combining traditional literature curation with a data-driven approach that predicts molecular activities from transcriptomics data, we constructed an initial COPD network model set based on a previously published non-diseased lung-relevant model set. The crowd was given the opportunity to enhance and refine the networks on a website (https://bionet.sbvimprover.com/) and to add mechanistic detail, as well as critically review existing evidence and evidence added by other users, so as to enhance the accuracy of the biological representation of the processes captured in the networks. Finally, scientists and experts in the field discussed and refined the networks during an in-person jamboree meeting. Here, we describe examples of the changes made to three of these networks: Neutrophil Signaling, Macrophage Signaling, and Th1-Th2 Signaling. We describe an innovative approach to biological network construction that combines literature and data mining and a crowdsourcing approach to generate a comprehensive set of COPD-relevant models that can be used to help understand the mechanisms related to lung pathobiology. Registered users of the website can freely browse and download the networks