171,027 research outputs found
Cycle time optimization by timing driven placement with simultaneous netlist transformations
We present new concepts to integrate logic synthesis and physical design. Our methodology uses general Boolean transformations as known from technology-independent synthesis, and a recursive bi-partitioning placement algorithm. In each partitioning step, the precision of the layout data increases. This allows effective guidance of the logic synthesis operations for cycle time optimization. An additional advantage of our approach is that no complicated layout corrections are needed when the netlist is changed
A3 thinking approach to support knowledge-driven design
Problem solving is a crucial skill in product development. Any lack of effective decision making at an early design stage will affect productivity and increase costs and the lead time for the other stages of the product development life cycle. This could be improved by the use of a simple and informative approach which allows the designers and engineers to make decisions in product design by providing useful knowledge. This paper presents a novel A3 thinking approach to problem solving in product design, and provides a new A3 template which is structured from a combination of customised elements (e.g. the 8 Disciplines approach) and reflection practice. This approach was validated using a case study in the Electromagnetic Compatibility (EMC) design issue for an automotive electrical sub-assembly product. The main advantage of the developed approach is to create and capture the useful knowledge in a simple manner. Moreover, the approach provides a reflection section allowing the designers to turn their experience of design problem solving into proper learning and to represent their understanding of the design solution. These will be systematically structured (e.g. as a design checklist) to be circulated and shared as a reference for future design projects. Thus, the recurrence of similar design problems will be prevented and will aid the designers in adopting the expected EMC test results
Manipulating Attributes of Natural Scenes via Hallucination
In this study, we explore building a two-stage framework for enabling users
to directly manipulate high-level attributes of a natural scene. The key to our
approach is a deep generative network which can hallucinate images of a scene
as if they were taken at a different season (e.g. during winter), weather
condition (e.g. in a cloudy day) or time of the day (e.g. at sunset). Once the
scene is hallucinated with the given attributes, the corresponding look is then
transferred to the input image while preserving the semantic details intact,
giving a photo-realistic manipulation result. As the proposed framework
hallucinates what the scene will look like, it does not require any reference
style image as commonly utilized in most of the appearance or style transfer
approaches. Moreover, it allows to simultaneously manipulate a given scene
according to a diverse set of transient attributes within a single model,
eliminating the need of training multiple networks per each translation task.
Our comprehensive set of qualitative and quantitative results demonstrate the
effectiveness of our approach against the competing methods.Comment: Accepted for publication in ACM Transactions on Graphic
Application of shape grammar theory to underground rail station design and passenger evacuation
This paper outlines the development of a computer design environment that generates station ‘reference’ plans for analysis by designers at the project feasibility stage. The developed program uses the theoretical concept of shape grammar, based upon principles of recognition and replacement of a particular shape to enable the generation of station layouts. The developed novel shape grammar rules produce multiple plans of accurately sized infrastructure faster than by traditional means. A finite set of station infrastructure elements and a finite set of connection possibilities for them, directed by regulations and the logical processes of station usage, allows for increasingly complex composite shapes to be automatically produced, some of which are credible station layouts at ‘reference’ block plan level. The proposed method of generating shape grammar plans is aligned to London Underground standards, in particular to the Station Planning Standards and Guidelines 5th edition (SPSG5 2007) and the BS-7974 fire safety engineering process. Quantitative testing is via existing evacuation modelling software. The prototype system, named SGEvac, has both the scope and potential for redevelopment to any other country’s design legislation
A New Paradigm in Split Manufacturing: Lock the FEOL, Unlock at the BEOL
Split manufacturing was introduced as an effective countermeasure against
hardware-level threats such as IP piracy, overbuilding, and insertion of
hardware Trojans. Nevertheless, the security promise of split manufacturing has
been challenged by various attacks, which exploit the well-known working
principles of physical design tools to infer the missing BEOL interconnects. In
this work, we advocate a new paradigm to enhance the security for split
manufacturing. Based on Kerckhoff's principle, we protect the FEOL layout in a
formal and secure manner, by embedding keys. These keys are purposefully
implemented and routed through the BEOL in such a way that they become
indecipherable to the state-of-the-art FEOL-centric attacks. We provide our
secure physical design flow to the community. We also define the security of
split manufacturing formally and provide the associated proofs. At the same
time, our technique is competitive with current schemes in terms of layout
overhead, especially for practical, large-scale designs (ITC'99 benchmarks).Comment: DATE 2019 (https://www.date-conference.com/conference/session/4.5
Learning to Generate Posters of Scientific Papers
Researchers often summarize their work in the form of posters. Posters
provide a coherent and efficient way to convey core ideas from scientific
papers. Generating a good scientific poster, however, is a complex and time
consuming cognitive task, since such posters need to be readable, informative,
and visually aesthetic. In this paper, for the first time, we study the
challenging problem of learning to generate posters from scientific papers. To
this end, a data-driven framework, that utilizes graphical models, is proposed.
Specifically, given content to display, the key elements of a good poster,
including panel layout and attributes of each panel, are learned and inferred
from data. Then, given inferred layout and attributes, composition of graphical
elements within each panel is synthesized. To learn and validate our model, we
collect and make public a Poster-Paper dataset, which consists of scientific
papers and corresponding posters with exhaustively labelled panels and
attributes. Qualitative and quantitative results indicate the effectiveness of
our approach.Comment: in Proceedings of the 30th AAAI Conference on Artificial Intelligence
(AAAI'16), Phoenix, AZ, 201
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