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Training Deep Neural Networks with Quantization and Structured Sparsity

Deep learning algorithms have shown tremendous success in applications ranging from self-driving cars to medical diagnosis. However, their usage on embedded platforms, such as mobile devices, has been limited due to high memory and computation requirements. The objective of this research is to compress neural network models in a hardware-friendly manner, while retaining their performance.