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- W3138766978 abstract "Grouped convolutions are a drop-in replacement for standard convolutional layers in neural networks. With an adjustable scaling parameter - the number of groups g - they reduce the number of parameters and multiply-accumulate operations (MACs) at the expense of representational power. However, current implementations of grouped convolutions do not perform optimally. In this paper we discuss Grouped Spatial Packed Convolutions (GSPC), a new approach to grouped convolutions, implemented in TVM’s tensor compute language. We analyse a set of networks leveraging the full range of the g parameter, and evaluate their performance in terms of inference time. We observe that GSPC achives the best performance in all settings, improving the existing implementations of grouped convolutions in TVM, PyTorch and TensorFlow Lite by 3.4x, 8x and 4x on average respectively. Code is available at https://github.com/gecLAB/tvm-GSPC/" @default.
- W3138766978 created "2021-03-29" @default.
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- W3138766978 date "2020-01-01" @default.
- W3138766978 modified "2023-09-23" @default.
- W3138766978 title "Evaluating Grouped Spatial Pack Convolutions on Edge CPUs" @default.
- W3138766978 hasPublicationYear "2020" @default.
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