  In this paper we investigate the amount of spatial context required for
channel attention. To this end we study the popular squeeze-and-excite (SE)
block which is a simple and lightweight channel attention mechanism. SE blocks
and its numerous variants commonly use global average pooling (GAP) to create a
single descriptor for each channel. Here, we empirically analyze the amount of
spatial context needed for effective channel attention and find that limited
localcontext on the order of seven rows or columns of the original image is
sufficient to match the performance of global context. We propose tiled
squeeze-and-excite (TSE), which is a framework for building SE-like blocks that
employ several descriptors per channel, with each descriptor based on local
context only. We further show that TSE is a drop-in replacement for the SE
block and can be used in existing SE networks without re-training. This implies
that local context descriptors are similar both to each other and to the global
context descriptor. Finally, we show that TSE has important practical
implications for deployment of SE-networks to dataflow AI accelerators due to
their reduced pipeline buffering requirements. For example, using TSE reduces
the amount of activation pipeline buffering in EfficientDetD2 by 90% compared
to SE (from 50M to 4.77M) without loss of accuracy. Our code and pre-trained
models will be publicly available.
