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Found 3 papers, 1 papers with code
Dense Pruning of Pointwise Convolutions in the Frequency Domain
Our key insights are that 1) pointwise convolutions commute with frequency transformation and thus can be computed in the frequency domain without modification, 2) each channel within a given layer has a different level of sensitivity to frequency domain pruning, and 3) each channel's sensitivity to frequency pruning is approximately monotonic with respect to frequency.
EVA$^2$: Exploiting Temporal Redundancy in Live Computer Vision
Hardware support for deep convolutional neural networks (CNNs) is critical to advanced computer vision in mobile and embedded devices.
Reconfiguring the Imaging Pipeline for Computer Vision
We propose a new image sensor design that can compensate for skipping these stages.
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