  Multi-head self-attention forms the core of Transformer networks. However,
their quadratically growing complexity with respect to the input sequence
length impedes their deployment on resource-constrained edge devices. We
address this challenge by proposing a dynamic pruning method, which exploits
the temporal stability of data across tokens to reduce inference cost. The
threshold-based method only retains significant differences between the
subsequent tokens, effectively reducing the number of multiply-accumulates, as
well as the internal tensor data sizes. The approach is evaluated on the Google
Speech Commands Dataset for keyword spotting, and the performance is compared
against the baseline Keyword Transformer. Our experiments show that we can
reduce ~80% of operations while maintaining the original 98.4% accuracy.
Moreover, a reduction of ~87-94% operations can be achieved when only degrading
the accuracy by 1-4%, speeding up the multi-head self-attention inference by a
factor of ~7.5-16.
