# What is the advantage of using Google's Coral over Nvidia's Xavier?

I was reading about the possibility of using Google's Coral for deep learning-based object detection and image classification. I heard it has a good speed in terms of frames/sec.

I also read that Google's Coral is only compatible with quantized models. What does this mean? How will this affect the performance of object detection or classification in terms of accuracy and speed?

Deep learning models are essentially collections of real-valued numbers. Because there are infinitely many real numbers, computers represent them using a format call 'floating point' numbers, which are not completely accurate. For example, a 32-bit floating point number can only represent at most $$2^{32}$$ distinct values. In contrast, a 64-bit floating point number can represent $$2^{64}$$ distinct values.