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I was afraid you would claim that, as it is a very common misunderstanding. The only distributional assumption in linear regression is that the residuals are normally distributed. Even that assumption can mostly be ignored if your dataset is sufficiently large (> 30 or > 100 depending on who you ask). There is no assumption about the distribution of independent variables. A good reason for transforming an independent variable is to make the effect of that variable linear, but making the distribution normal is not a good reason to transform the variable.
Think about what a normal distribution is: it is a distribution of a continuous variable. Now think about your the distribution of your size of board variable, it is discrete so it can take only values 0,1,2,3,... There is no transformation that can transform the latter in the former (without adding random noise). So it is logically impossible to make your variable normally distributed. Fortunately, it is not necessary, as I showed above.
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