Deep learning is witnessing a growing history of success. However, the large/heavy models that must be run on a high-performance computing system are far from optimal. Artificial intelligence is already widely used in business applications. The computational demands of AI inference and training are increasing. As a result, a relatively new class of deep learning approaches known as quantized neural network models has emerged to address this disparity. Memory has been one of the biggest challenges for deep learning architectures. It was an evolution of the gaming industry that led to the rapid development of hardware leading to GPUs, enabling 50 layer networks of today. Still, the hunger for memory by newer and powerful networks is now pushing for evolutions of Deep Learning model compression techniques to put a leash on this requirement, as AI is quickly moving towards edge devices to give near to real-time results for captured data. Model quantization is one such rapidly growing technology that has allowed deep learning models to be deployed on edge devices with less power, memory, and computational capacity than a full-fledged computer.

How Did AI Migrate From Cloud to Edge?

Many businesses use clouds as their primary AI engine. It can host required data via a cloud data center for performing intelligent decisions. This process of uploading data to cloud storage and interaction with data centers induces a delay in making real-time decisions. The cloud will not be a viable choice in the future as demand for IoT applications and their real-time responses grows. As a result, AI on the edge is becoming more popular.

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