Kernel Perceptron vs Polynomial Perceptron
A SVM with a polynomial kernel is a SVM classifier.

A kernel perceptron is a perceptron classifier, or in other words, a neural net.

A SVM is quite different from a neural net.  So, that's one way that they differ.

However, Wikipedia says that SVMs are in some respects a generalization of a kernel perceptron, generalized with regularization.  Regularization is basically a form of Occam's razor: it says that, all else being equal, simpler models are preferred over more complex models.  Regularization helps avoid overfitting and thus is very useful in practice.

(Related: See also Support Vector Machines as Neural Nets? -- a SVM with a linear kernel is similar to a single-layer perceptron classifier, in case that's what you were thinking of.)