Deep Learning is a promising solution for most of the AI/ML applications in various fields like safety and transportation, medical field, weather forecasting and many more. State-of-the- art deep neural networks can have hundreds of millions of parameters, and it makes them less than ideal for mass adoption in devices with constrained memory and power requirements like edge computing devices and mobile devices. Techniques like quantization and inducing sparsity, aims to reduce the total number of computations needed for deep learning inference.
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