Brain-inspired event-driven processors execute deep neural networks (DNNs) in a sparsity-
aware manner. Specifically, if more zeros are induced in the activation maps, less
computation will be performed in the succeeding convolution layer. However, inducing
activation sparsity in DNNs remains a challenge. To address this, we propose a training
approach STAR (Sparse Thresholded Activation under partial-Regularization), which
combines activation regularization with thresholding, to overcome the barrier of a single …