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Samuel S Cho

    Samuel S Cho

    Various smart objects are connected in the invisible and intelligent Internet of Things (IoT) network to share information. However, considering the heterogeneity of Internet of Things devices, connecting devices with differences in... more
    Various smart objects are connected in the invisible and intelligent Internet of Things (IoT) network to share information. However, considering the heterogeneity of Internet of Things devices, connecting devices with differences in capabilities-processing, storage, energy, and communication bandwidth- and programming methods-language, compiler, and tools-can burden developers with the complexities caused by interoperability. This paper proposes a solution to address this interoperability issue when sharing information among IoT devices. We model information sharing as the communication between a sender and a receiver with constraints from application requirements. We explore tradeoffs from constraints to propose three strategies to hide technical details of various data representations to meet their application needs. We propose the Chitchat Information Sharing Language (CISL) for developing IoT applications so that IoT developers can focus solely on their applications by delegatin...
    An algorithm for technology mapping of combinational and sequential logic networks is proposed and applied to mapping into K-input lookup-tables (K-LUTs). The new algorithm avoids the hurdle of computing all K-input cuts while preserving... more
    An algorithm for technology mapping of combinational and sequential logic networks is proposed and applied to mapping into K-input lookup-tables (K-LUTs). The new algorithm avoids the hurdle of computing all K-input cuts while preserving the quality of the results, in terms of area and depth. The memory and runtime of the proposed algorithm are linear in circuit size and quite affordable even for large industrial designs. For example, computing a good quality 6-LUT mapping of an AIG with 1 M nodes takes 150 Mb of RAM and 1 minute on a typical laptop. An extension of the algorithm allows for sequential mapping, which searches the combined space of all possible mappings and retimings. This leads to an 18-22% improvement in depth with a 3-5% LOT count penalty, compared to combinational mapping followed by retiming.