Is there any conceptual relationship between 'kernel' in SVM and 'kernel' in convolution neural net?
There is no direct relationship between these two concepts. However we can find some indirect ones.

According to Merriam Webster, 


  kernel means a central or essential part


which hints why they are called "kernel". Specifically, deciding "how to measure point-point similarity (a.k.a. kernel function)" is the central part of kernel methods, and deciding "what array, matrix, or tensor (a.k.a. kernel matrix) to be convoluted with a data point" is the central part of convolutional neural networks.

A kernel function receives two data points, implicitly maps them into a higher (possibly infinite) dimension, then calculates their inner product.

A kernel matrix (or array, or tensor) is convoluted with one data point to map the data point explicitly into an often lower dimension. Here, we are ignoring a subtle difference between filter and kernel (a filter is composed of one kernel per channel).

Therefore, these two concepts are indirectly related based on mapping to a new representation. However, 


Kernel functions map implicitly, but kernel matrices map explicitly,
Kernel functions cannot be stacked over each other (shallow representation), but kernel matrices can be since the input and output (explicit representations) has the same structure (deep representation),
The non-linearity of map is integrated into kernel functions, but for kernel matrices, we should apply a non-linear activation function after the (input, kernel) convolution to reach a similar non-linearity,
Implicit representations cannot be learned for kernel functions, a specific function implies a specific representation. However, for kernel matrices, representations can be learned by adjusting (learning) the weights of kernels, and can also be enriched by stacking kernels over each other.