Abstract Deep learning, as one of the most currently remarkable machine learning techniques, has achieved great success in many fields such as speech recognition, image analysis, and autonomous driving. However, the neural network requires billions of multiply-and-accumulated operations, which makes the single-frame runtime enormous and energy-hungry. To optimize these imperfections, researchers from the University of Zurich and ETH Zurich developed a hardware accelerator named NullHop, which is a flexible and efficient hardware accelerator architecture aiming at exploiting the sparsity of neuron activations. NullHop uses a novel sparse matrix compression algorithm to encode the input data into two elements: a Sparsity Map (SM) and a Non-Zero Value List (NZVL).