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While speeding up cryptography tasks can be accomplished by using a multi-core architecture to parallelize computation, one of the major challenges is optimizing power consumption. In principle, depending on the computation workload, individual cores can be turned off to save power during operation. However, too few active cores may lead to computational bottlenecks. In this work, we propose a novel platform named Spike-MCryptCores: a low-power multi-core AES platform with a neuromorphic controller. The proposed Spike-MCryptCores platform is composed of multiple AES cores, each core is equipped with a clock-gating scheme for reducing its power consumption while being idle. To optimize the power consumption of the whole platform, we use a neuromorphic controller. Therefore, a comprehensive framework to generate a data set, train the neural network, and produce hardware configuration for the Spiking Neural Network (SNN), a brain-inspired computing paradigm, is also presented in this paper. Moreover, Spike-MCryptCores integrates the hardware SNN inside its architecture to support low-cost and low-latency adaptations. The results show that implemented SNN controller occupies only 2.3 % of the overall area cost while providing the ability to reduce power consumption significantly. The lightweight SNN controller model is trained and tested with up to 95 % accuracy. The maximum difference between the predicted number of cores and the ideal one from the label is one unit only. Under 24 test scenarios, a SNN controller with clock-gating helps Spike-MCryptCores reducing the power consumption by 48.6 % on the average; by 67 % for the best-case scenario, and by 39 % for the worst-case scenario. [ABSTRACT FROM AUTHOR]