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A New Reconfigurable Architecture with Applications to IoT and Mobile Computing

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dc.contributor.author Gharehbaghi, Amir Masoud
dc.contributor.author Maruoka, Tomohiro
dc.contributor.author Fujita, Masahiro
dc.date.accessioned 2021-12-13T01:19:24Z
dc.date.available 2021-12-13T01:19:24Z
dc.date.issued 2019
dc.identifier.citation Gharehbaghi A.M., Maruoka T., Fujita M. (2019) A New Reconfigurable Architecture with Applications to IoT and Mobile Computing. In: Strous L., Cerf V. (eds) Internet of Things. Information Processing in an Increasingly Connected World. IFIPIoT 2018. IFIP Advances in Information and Communication Technology, vol 548. Springer, Cham. https://doi.org/10.1007/978-3-030-15651-0_12 en_US
dc.identifier.isbn 978-3-030-15651-0
dc.identifier.uri ${sadil.baseUrl}/handle/123456789/2196
dc.description 14 p. ; PDF en_US
dc.description.abstract Traditional reconfigurable devices known as FPGAs utilize a complicated programmable routing network to provide flexibility in connecting different logic elements across the FPGA chip. As such, the routing procedure may become very complicated, especially in the presence of tight timing constraints. Moreover, the routing network itself occupies a large portion of chip area as well as consumes a lot of power. Therefore, limiting their usage in mobile applications or IoT devices with higher performance and lower energy demands. In this paper, we introduce a new reconfigurable architecture which only allows communication between neighboring logic elements. This way, the routing structure and the routing resources become much simpler than traditional FPGAs. Moreover, we present two different method for scheduling and routing in our new proposed architecture. The first method deals with general circuits or irregular computations and is based on integer linear programming. The second method is for regular computations such as convolutional neural networks or matrix operations. We have shown the mapping results on ISCAS benchmark circuits as general irregular computations as well as heuristics to improve the efficiency of mapping for larger benchmarks. Moreover, we have shown results on regular computations including matrix multiplication and convolution operations of neural networks. en_US
dc.language.iso en en_US
dc.publisher Springer Nature en_US
dc.subject Reconfigurable architecture en_US
dc.subject Placement and routing en_US
dc.subject Mobile computing en_US
dc.subject Convolutional neural network en_US
dc.title A New Reconfigurable Architecture with Applications to IoT and Mobile Computing en_US
dc.type Book en_US


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