%0 Conference Proceedings
%T A New Reconfigurable Architecture with Applications to IoT and Mobile Computing
%+ The University of Tokyo (UTokyo)
%+ The Jikei University School of Medicine
%A Gharehbaghi, Amir, Masoud
%A Maruoka, Tomohiro
%A Fujita, Masahiro
%Z Part 3: IFIPIoT 2018 Refereed Papers
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B 1st IFIP International Internet of Things Conference (IFIPIoT)
%C Poznan, Poland
%Y Leon Strous
%Y Vinton G. Cerf
%I Springer International Publishing
%3 Internet of Things. Information Processing in an Increasingly Connected World
%V AICT-548
%P 133-146
%8 2018-09-18
%D 2018
%R 10.1007/978-3-030-15651-0_12
%K Reconfigurable architecture
%K Placement and routing
%K FPGA
%K Mobile computing
%K IoT
%K Convolutional neural network
%Z Computer Science [cs]Conference papers
%X 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.
%G English
%2 https://inria.hal.science/hal-03217363/document
%2 https://inria.hal.science/hal-03217363/file/481032_1_En_12_Chapter.pdf
%L hal-03217363
%U https://inria.hal.science/hal-03217363
%~ IFIP
%~ IFIP-AICT
%~ IFIP-AICT-548
%~ IFIP-IOT