why delay/latency of the neural net is considered in yolo_kp example and not considered in netx/mnist example

[gwgknudayanga]

Hi Experts,

In the netx/yolo_kp example , with the following code they used two buffers saying that "There is a latency in the prediction equal to the number of layers the network has and the encoding step. Two FIFO buffers are used to synchronize the input frame and target annotation with the predicted output."

frame_buffer = netx.modules.FIFO(depth=len(net) + 1)
annotation_buffer = netx.modules.FIFO(depth=len(net) + 1)

But in netx/mnist there is no such consideration for such a latency due to the layers of the neural network.

Actually when we consider one time step, the input to the network should be completely propagated to the output of the neural network at the end of that time step. If this is the case, why we worry about additional offset/delay which equals to the len(net) in yolo_kp? This is very confusing.

Thanks and Rgds,
Udayanga