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.

翻译：过去几年中,由于诸如神经网络及其变体等网络,计算机视野领域发展非常迅速。 存储模型和计算成本所需的内存对于这样一个网络限制在边缘设备上部署来说非常高。 许多时候, 应用程序依赖于云层, 但是由于往返延迟, 使得实时工作困难。 我们通过在边缘设备上部署神经网络来克服这些问题。 边设备计算成本通过降低模型参数的浮点精确度而减少。 在此之后, 模型减少所需的内存和计算速度加快, 而模型的性能最不受影响。 这使得边设备能够从神经网络中自行预测。