Robert Shearer

ORCID: 0000-0003-3739-9388
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About
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Research Areas
  • Embedded Systems Design Techniques
  • Parallel Computing and Optimization Techniques
  • Interconnection Networks and Systems
  • Logic, Reasoning, and Knowledge
  • Software Engineering Research
  • Semantic Web and Ontologies
  • Natural Language Processing Techniques

University of Oxford
2009

We present a novel reasoning calculus for the description logic SHOIQ^+---a knowledge representation formalism with applications in areas such as Semantic Web. Unnecessary nondeterminism and construction of large models are two primary sources inefficiency tableau-based calculi used state-of-the-art reasoners. In order to reduce nondeterminism, we base our on hypertableau hyperresolution calculi, which extend blocking condition ensure termination. size constructed models, introduce anywhere...

10.1613/jair.2811 article EN cc-by Journal of Artificial Intelligence Research 2009-10-28

Domain-specific SoCs (DSSoCs) are an attractive solution for domains with extremely stringent power, performance, and area constraints. However, DSSoCs suffer from two fundamental complexities. On the one hand, their many specialized hardware blocks result in complex systems thus high development effort. other system knobs expand complexity of design space, making search optimal difficult. Thus to reach prevalence, taming such complexities is necessary. To address these challenges, this...

10.1145/3544016 article EN ACM Transactions on Embedded Computing Systems 2022-06-16

Domain-specific SoCs (DSSoCs) are attractive solutions for domains with stringent power/performance/area constraints; however, they suffer from two fundamental complexities. On the one hand, their many specialized hardware blocks result in complex systems and thus high development effort. other, system knobs expand complexity of design space, making search optimal difficult. Thus to reach prevalence, taming such complexities is necessary. This work identifies necessary features an...

10.48550/arxiv.2201.05232 preprint EN cc-by-nc-sa arXiv (Cornell University) 2022-01-01
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