How can I implement SVM classification function on embedded platform after it has been trained in MATLAB.

I have trained SVM with training data using the fitcsvm() function. SVMModel = fitcsvm(X,y,'KernelScale',.5,'Standardize',true,'OutlierFraction',0.05); with output: SVMModel =

ClassificationSVM
    PredictorNames: {'x1'  'x2'}
      ResponseName: 'Y'
        ClassNames: 1
    ScoreTransform: 'none'
   NumObservations: 900
             Alpha: [460x1 double]
              Bias: -28.3591
  KernelParameters: [1x1 struct]
                Mu: [0.6169 0.1173]
             Sigma: [0.1381 0.0132]
    BoxConstraints: [900x1 double]
   ConvergenceInfo: [1x1 struct]
   IsSupportVector: [900x1 logical]
            Solver: 'SMO'

I am satisfied with the results of predict() function, implemented as:

[~,score] = predict(SVMModel,[X1(:),X2(:)]);

Now I want to calculate the score of an individual sample on an embedded platform with limited resources. How may I program the SVM-Model as well as the classification function on a system with say, 1 MegaBytes maximum ram memory?

It is also not clear how may I know the parameters like 'Alpha' and 'Bias' as these will be needed when writing the classification function.

I have no training in machine learning and therefore I have only minimum understanding of the technicalities of SVM algorithm. Apologies if the question seems to naive.