Resources utilization problem with opencl kernel

I am using Opencl to accelerate updateWeight function (for neural network) on FPGA. I use the follwing kernel

__kernel void layerUpdateWeights_kernel(__global const double* inputs,
                        __global const double* delta,
                        __global double* weightsMat,
                        const int inputsSize,
                                        const double learningRate ) {

    int k = get_global_id(0);
    int j = get_global_id(1);

        weightsMat[k * (inputsSize + 1) + j] += learningRate * delta[k] * inputs[j]; 

and this what I wrote in the host code :

cl::NDRange globalSize(nbNeuronal, inputsSize+1);

err = q.enqueueNDRangeKernel(updateWeightsKernel, cl::NullRange, globalSize, cl::NullRange);

after implementation on FPGA I got a correct results and I determined the resources utilization using vivado tools.

I know that this code will create nbNeuronal*(inputSize+1) workItems on the FPGA. So, the resources on the FPGA will depend on this two values.

But, when I apply this code with for example nbNeuronal =10 and nbNeuronal = 200, I find the same resources utilization on FPGA (LUT, DSP, BRAM, ….).

any help please