Abstract
For a given set S of n real numbers and a positive integer \(k<n\), there are totally \({n \atopwithdelims ()k}\) subsets of S with k elements. Among these subsets, to find L subsets whose summation of their elements are the L smallest is so called the L smallest k-subsets sum problem. It is widely applied in the real operations and computational research. However it is very computationally challenging to process large scale L smallest k-subsets sum problem. To solve this problem, this paper presents a FPGA implementation of a finite-time convergent recurrent neural network of L smallest k-subsets sum problem. And the neural network model is tested on a Digilent Nexys 4 DDR board with Xilinx Artix 7 FPGA. Experimental results show that the proposed hardware implementation method has a high degree of parallelism and fast performance.
The work described in the paper was supported by the National Science Foundation of China under Grant 61503233.
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Gu, S., Wang, X. (2017). FPGA Implementation of the L Smallest k-Subsets Sum Problem Based on the Finite-Time Convergent Recurrent Neural Network. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10261. Springer, Cham. https://doi.org/10.1007/978-3-319-59072-1_40
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DOI: https://doi.org/10.1007/978-3-319-59072-1_40
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