$1.$ Network Architecture:

Input Layer: $4$ inputs $($each input is $8-$bit wide$)$

Hidden Layer: $3$ neurons, each with a ReLU activation function.

Output Layer: $2$ outputs $($each output is $8-$bit wide$),$ representing two classes.

$2.$ Weights and Biases:

For simplicity, use $8-$bit signed integers for weights and biases.

$3.$ Activation Function:

Implement the ReLU activation function in Verilog: ReLU$($x$)=$ max$(0,$ x$).$

$4.$ Task:

Implement the feedforward pass of the network, Calculate the outputs of the network for a given input set.

$5.$ FPGA Implementation:

Synthesize and implement the design on a specific FPGA platform, Use simulation tools to verify the correctness of the implementation.