I worked on an SMT-based superoptimizer research project[1] as an undergrad. We were targeting a weird stack-based chip so the details were pretty different, but it worked surprisingly well. Really fun project for sure!
SMT solvers are an incredible general-purpose tool for theses things, and we got quite far through various encoding tricks like synthesizing with much smaller bit-widths and then verifying with full widths. However, my impression is that while SMT solvers are great starting out, at some point the overhead and complexity of relying on encoding tricks starts to outweigh the advantages, to the point where either an entirely custom algorithm or some mix of existing solvers with a custom algorithm are the way to go. I don't know if that's fundamental, or it's just a function of people being able to understand their own algorithms better than SMT solvers—most people I talked to saw SMT solver performance as a black box.
I remember reading about the Sketch synthesizer, which used a custom solver as its backend. It comes to mind because the first example of using Sketch[2] that our professor showed us involved SIMD shuffling instructions. It's probably worth checking out on its own—I remember its input language being pretty easy to use—but I can't find the project itself anywhere any more, probably because it used to be hosted on BitBucket :/.
I started with a solver-heavy approach whereby the SMT solver was trusted to come up with all sorts of things. By a process of steady refinement, my experience is that if there's a change whereby you trust the solver less and your own optimizations more, you do it. So now I call the SMT solver a gazillion times with much more simple problems. I also use the SMT solver in a bunch of different ways to invent general and specific (i.e. knowing the workload) optimizations for the problem which I then can apply without intervention of a solver.
I may ultimately wind up throwing SMT under the bus and doing my own bit-blasting; QF_BV ("the quantifier-free bit-vector logic") is not one of SMT's more difficult corners, and I may get some benefit in specializing my own bit-blast and talking straight to a SAT solver.
I have been doing a fair bit of work to try to reduce the 'black box' aspect of SMT solver performance, but SMT is hard to get a handle on it. One minute, you're solving Sudoko and logic problems ("The professor lives in the same street as the baker, while the man who lives in the blue house does not ride a bike, ..."). The next, staring at pages and pages of math that's aimed at people who want to be researchers in SMT - there's not much intermediate-level stuff aimed at the practitioners.
SMT solvers are an incredible general-purpose tool for theses things, and we got quite far through various encoding tricks like synthesizing with much smaller bit-widths and then verifying with full widths. However, my impression is that while SMT solvers are great starting out, at some point the overhead and complexity of relying on encoding tricks starts to outweigh the advantages, to the point where either an entirely custom algorithm or some mix of existing solvers with a custom algorithm are the way to go. I don't know if that's fundamental, or it's just a function of people being able to understand their own algorithms better than SMT solvers—most people I talked to saw SMT solver performance as a black box.
I remember reading about the Sketch synthesizer, which used a custom solver as its backend. It comes to mind because the first example of using Sketch[2] that our professor showed us involved SIMD shuffling instructions. It's probably worth checking out on its own—I remember its input language being pretty easy to use—but I can't find the project itself anywhere any more, probably because it used to be hosted on BitBucket :/.
[1]: https://jelv.is/chlorophyll.pdf
[2]: https://homes.cs.washington.edu/~bodik/ucb/cs294/fa12/Lectur...