  Spiking Neural Networks (SNNs) are an emerging domain of biologically
inspired neural networks that have shown promise for low-power AI. A number of
methods exist for building deep SNNs, with Artificial Neural Network
(ANN)-to-SNN conversion being highly successful. MaxPooling layers in
Convolutional Neural Networks (CNNs) are an integral component to downsample
the intermediate feature maps and introduce translational invariance, but the
absence of their hardware-friendly spiking equivalents limits such CNNs'
conversion to deep SNNs. In this paper, we present two hardware-friendly
methods to implement Max-Pooling in deep SNNs, thus facilitating easy
conversion of CNNs with MaxPooling layers to SNNs. In a first, we also execute
SNNs with spiking-MaxPooling layers on Intel's Loihi neuromorphic hardware
(with MNIST, FMNIST, & CIFAR10 dataset); thus, showing the feasibility of our
approach.
