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In this article, large\u2010scale analog SNNs are investigated and optimized at the hardware\u2010level by using SNNSim, the novel simulator for SNNs that employ analog synaptic devices and integrate\u2010and\u2010fire (I&amp;F) neuron circuits. SNNSim is a reconfigurable simulator that accurately and very quickly models the behavior of the user\u2010defined device characteristics and returns key metrics such as area, accuracy, latency, and power consumption as output. Notably, SNNSim exhibits exceptional efficiency, as it can process the entire 10\u2009000 Modified National Institute of Standards and Technology (MNIST)\u00a0test dataset in a few seconds, whereas SPICE simulations require hours to simulate a single MNIST test data. Using SNNSim, the conversion of artificial neural networks (ANNs) to SNNs is simulated and the performance of the large\u2010scale analog SNNs is optimized. The results enable the design of accurate, high\u2010speed, and low\u2010power operation of large\u2010scale SNNs. 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