RepGhost: A Hardware-Efficient Ghost Module via Re-parameterization. (arXiv:2211.06088v1 [cs.CV])

Feature reuse has been a key technique in light-weight convolutional neural
networks (CNNs) design. Current methods usually utilize a concatenation
operator to keep large channel numbers cheaply (thus large network capacity) by
reusing feature maps from other layers. Although concatenation is parameters-
and FLOPs-free, its computational cost on hardware devices is non-negligible.
To address this, this paper provides a new perspective to realize feature reuse
via structural re-parameterization technique. A novel hardware-efficient
RepGhost module is proposed for implicit feature reuse …

arxiv ghost hardware