What is the 1 Unit in the contraint of SVM: $y_i(wx_i+b) \geq1$
It is a unit of distance, I would usually assume euclidean distance.

In more detail:

The data point $x_i$ is projected onto the vector $w$, which defines the orientation of the discriminating linear hyperplane as it is orthogonal to $w$. Where the discriminating hyperplane is "fixed" along the orientation of $w$ is decided by the bias Term $b$.

So for classifying a positive data point $x_{+}$ we would want $wx_{+} + b$ to a positive number.

For a negative $x_{-}$ datapoint $wx_{-}+b$ should be negative.

We can combine these two disired outcomes into a single constraint by using the labels, so we want $y_{+}(wx_{+}+b) \geq 0$ and $y_{-}(wx_{-}+b) \geq 0$.

By replacing the $0$ with a positive value $d \geq 0$, we can alter the constraint to encode our desire that the points should be $d$ far away from from the discriminating hyperplane, giving the constraint is $y_i(wx_i +b) \geq d$.

What kind of unit of distance depends on the vector space. Usually orthogonal projections in euclidean vector spaces are used, in that case it would be euclidean distance.