  The field of computer vision has grown very rapidly in the past few years due
to networks like convolution neural networks and their variants. The memory
required to store the model and computational expense are very high for such a
network limiting it to deploy on the edge device. Many times, applications rely
on the cloud but that makes it hard for working in real-time due to round-trip
delays. We overcome these problems by deploying the neural network on the edge
device itself. The computational expense for edge devices is reduced by
reducing the floating-point precision of the parameters in the model. After
this the memory required for the model decreases and the speed of the
computation increases where the performance of the model is least affected.
This makes an edge device to predict from the neural network all by itself.
