  Computer vision has become increasingly prevalent in solving real-world
problems across diverse domains, including smart agriculture, fishery, and
livestock management. These applications may not require processing many image
frames per second, leading practitioners to use single board computers (SBCs).
Although many lightweight networks have been developed for mobile/edge devices,
they primarily target smartphones with more powerful processors and not SBCs
with the low-end CPUs. This paper introduces a CNN-ViT hybrid network called
SBCFormer, which achieves high accuracy and fast computation on such low-end
CPUs. The hardware constraints of these CPUs make the Transformer's attention
mechanism preferable to convolution. However, using attention on low-end CPUs
presents a challenge: high-resolution internal feature maps demand excessive
computational resources, but reducing their resolution results in the loss of
local image details. SBCFormer introduces an architectural design to address
this issue. As a result, SBCFormer achieves the highest trade-off between
accuracy and speed on a Raspberry Pi 4 Model B with an ARM-Cortex A72 CPU. For
the first time, it achieves an ImageNet-1K top-1 accuracy of around 80% at a
speed of 1.0 frame/sec on the SBC. Code is available at
https://github.com/xyongLu/SBCFormer.
