Jiang LI†a) , Student Member, Yusuke ATSUMARI †b) , Hiromasa KUBO †c) , Yuichi OGISHIMA †d) , Satoru YOKOTA †e) , Nonmembers, Hakaru TAMUKOH † †f) , and Masatoshi SEKINE †g) , Members SUMMARY A processing system with multiple field programmable gate array (FPGA) cards is described. Each FPGA card can interconnect using six I/O (up, down, left, right, front, and back) terminals. The communication network among FPGAs is scalable according to user design. When the system operates multi-dimensional applications, transmission efficiency among FPGA improved through user-adjusted dimensionality and network topologies for different applications. We provide a fast and flexible circuit configuration method for FPGAs of a multi-dimensional FPGA array. To demonstrate the effectiveness of the proposed method, we assess performance and power consumption of a circuit that calculated 3D Poisson equations using the finite difference method.
Mobile systems are used in various environments. Thus, it is practical for image recognition systems to autonomously learn template images that are adaptive to objects in their various environments. However, learning the features of such objects requires large-scale computation and complex control. Hence, we propose an image recognition system that selects and learns regions that have a given object's features. This system is designed as a hardware/software (hw/sw) complex system with the multi-dimensional field programmable gate array (FPGA) "Vocalise." This study discusses the possibility of dynamically building image databases and of real-time learning using the proposed image recognition system. Results indicate that the learning speed of the proposed method is estimated to be 1.4 × 10 3 faster than that obtained with a conventional software method. This suggests the possibility of real-time learning.
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