2022
DOI: 10.48550/arxiv.2202.02310
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EcoFlow: Efficient Convolutional Dataflows for Low-Power Neural Network Accelerators

Abstract: Dilated and transposed convolutions are widely used in modern convolutional neural networks (CNNs). These kernels are used extensively during CNN training and inference of applications such as image segmentation and high-resolution image generation. Although these kernels have grown in popularity, they stress current compute systems due to their high memory intensity, exascale compute demands, and large energy consumption. We find that commonly-used low-power CNN inference accelerators based on spatial archite… Show more

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