Feb. 13, 2024, 5:42 a.m. | Dimitrios Danopoulos Georgios Zervakis Dimitrios Soudris J\"org Henkel

cs.LG updates on arXiv.org arxiv.org

Vision Transformer (ViT) models which were recently introduced by the transformer architecture have shown to be very competitive and often become a popular alternative to Convolutional Neural Networks (CNNs). However, the high computational requirements of these models limit their practical applicability especially on low-power devices. Current state-of-the-art employs approximate multipliers to address the highly increased compute demands of DNN accelerators but no prior research has explored their use on ViT models. In this work we propose TransAxx, a framework based …

architecture art become cnns computational computing convolutional neural networks cs.ar cs.lg current devices low networks neural networks popular power practical requirements state transformer transformer architecture transformers vision vit

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