  Reducing energy consumption is a critical point for neural network models
running on edge devices. In this regard, reducing the number of
multiply-accumulate (MAC) operations of Deep Neural Networks (DNNs) running on
edge hardware accelerators will reduce the energy consumption during inference.
Spiking Neural Networks (SNNs) are an example of bio-inspired techniques that
can further save energy by using binary activations, and avoid consuming energy
when not spiking. The networks can be configured for equivalent accuracy on a
task through DNN-to-SNN conversion frameworks but their conversion is based on
rate coding therefore the synaptic operations can be high. In this work, we
look into different techniques to enforce sparsity on the neural network
activation maps and compare the effect of different training regularizers on
the efficiency of the optimized DNNs and SNNs.
