  Edge AI accelerators have been emerging as a solution for near customers'
applications in areas such as unmanned aerial vehicles (UAVs), image
recognition sensors, wearable devices, robotics, and remote sensing satellites.
These applications not only require meeting performance targets but also
meeting strict area and power constraints due to their portable mobility
feature and limited power sources. As a result, a column streaming-based
convolution engine has been proposed in this paper that includes column sets of
processing elements design for flexibility in terms of the applicability for
different CNN algorithms in edge AI accelerators. Comparing to a commercialized
CNN accelerator, the key results reveal that the column streaming-based
convolution engine requires similar execution cycles for processing a 227 x 227
feature map with avoiding zero-padding penalties.
