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

Optimizing the Consumption of Spiking Neural Networks with Activity Regularization