I'm not sure, but this seems a bit like how Julia specialize functions based on type of arguments? Or maybe it's the inverse - as Julia creates specialized functions for you (eg add can take numbers, but will be specialized for both int32 and int64 and execute via appropriate machine instructions).
In fact, I think Julia is a great example of taking some good parts of scheme and building a more conventional (in terms of syntax anyway) language on top.
Yes, Julia has some of it. But you're still required to specify the template parameters of a type (unless I'm mistaken). Whereas what I'm talking about is that any value of a data type could be compile time known. For example, some or all of the dimensions of an nd-array, as well some or all values of said nd-array.
Julia has explicit parameterization, but will also interprocedurally propagate known field values at compile time if known (which happens a lot more because our compile time is later), even if they weren't explicitly parameterized. Since this is so useful (e.g. as you say for dimensions of nd arrays - particularly in machine learning), there's been some talk of adding explicit mechanisms to control the implicit specialization also.
In fact, I think Julia is a great example of taking some good parts of scheme and building a more conventional (in terms of syntax anyway) language on top.