Equality-constrained quadratic programming (QP) has been one of the most basic and typical problems in the Internet of Things domain. In big data scenarios, how to quickly and accurately solve the problem in hardware has not been realized. Therefore, in this article, a memristive recurrent neural circuit that can parallel solve the QP problem in real time is proposed. First, a new memristive synaptic array is designed that can simultaneously implement parallel reading and writing. On the basis of this structure, a new neural network circuit based on memristor is designed that can perform large-scale recursive operations by parallel methods. This circuit can solve the equality-constrained QP problem in different situations by using such real-time programmable memristor arrays processing in memory. The PSpice simulation results show that the problem can be solved with 99.8% precision. Based on practical verification, the neural circuit experiment on PCB is presented with 97.34% precision. Moreover, the circuit has good robustness under the interference of weight value. And, it has an advantage in processing time compared with FPGA.

中文翻译：

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用于并行操作快速求解等式约束二次规划的忆阻递归神经网络电路


等式约束二次规划（QP）一直是物联网领域最基本、最典型的问题之一。在大数据场景下，如何快速、准确地解决硬件上的问题还没有实现。因此，本文提出了一种能够实时并行解决QP问题的忆阻循环神经电路。首先，设计了一种新的忆阻突触阵列，可以同时实现并行读写。在此结构的基础上，设计了一种基于忆阻器的新型神经网络电路，可以通过并行方法进行大规模递归运算。该电路可以利用存储器中的这种实时可编程忆阻器阵列处理来解决不同情况下的等式约束QP问题。 PSpice仿真结果表明该问题可以以99.8%的精度得到解决。经过实际验证，在PCB上进行神经电路实验，精度达到97.34%。而且该电路在重量值干扰下具有良好的鲁棒性。并且，与FPGA相比，它在处理时间上具有优势。