  Deep neural networks (DNNs) have been successfully applied in various fields.
In DNNs, a large number of multiply-accumulate (MAC) operations are required to
be performed, posing critical challenges in applying them in
resource-constrained platforms, e.g., edge devices. To address this challenge,
in this paper, we propose a class-based early-exit for dynamic inference.
Instead of pushing DNNs to make a dynamic decision at intermediate layers, we
take advantage of the learned features in these layers to exclude as many
irrelevant classes as possible, so that later layers only have to determine the
target class among the remaining classes. When only one class remains at a
layer, this class is the corresponding classification result. Experimental
results demonstrate the computational cost of DNNs in inference can be reduced
significantly with the proposed early-exit technique. The codes can be found at
https://github.com/HWAI-TUDa/EarlyClassExclusion.
