Computer Science > Neural and Evolutionary Computing
arXiv:2205.07076 (cs)
[Submitted on 14 May 2022]
Title:Spiking Approximations of the MaxPooling Operation in Deep SNNs
View a PDF of the paper titled Spiking Approximations of the MaxPooling Operation in Deep SNNs, by Ramashish Gaurav and 2 other authors
View PDFAbstract: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.
| Comments: | Accepted in IJCNN-2022 |
| Subjects: | Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) |
| Cite as: | arXiv:2205.07076 [cs.NE] |
| (orarXiv:2205.07076v1 [cs.NE] for this version) | |
| https://doi.org/10.48550/arXiv.2205.07076 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Spiking Approximations of the MaxPooling Operation in Deep SNNs, by Ramashish Gaurav and 2 other authors
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