  Efficient and compact neural network models are essential for enabling the
deployment on mobile and embedded devices. In this work, we point out that
typical design metrics for gauging the efficiency of neural network
architectures -- total number of operations and parameters -- are not
sufficient. These metrics may not accurately correlate with the actual
deployment metrics such as energy and memory footprint. We show that throughput
and energy varies by up to 5X across different neural network operation types
on an off-the-shelf Arm Cortex-M7 microcontroller. Furthermore, we show that
the memory required for activation data also need to be considered, apart from
the model parameters, for network architecture exploration studies.
