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Labs I've written for classes:
- sat-chaff: implement a basic DPLL SAT solver, profile it, then optimize
it with the watched literals trick from Chaff
- smt-symex: implement a bitblasting QF_ABV SMT solver and use it to build
a symbolic execution engine (builds on sat-chaff)
- static-analysis: implement a mini static analysis pass that finds some
bugs in the Linux kernel
There are some "work-in-progress labs:"
- neural-net: implement a simple feed-forward neural net training &
inference program. the code is kind of a mess right now, but it's the
smallest/simplest backprop implementation I know of. one of our 240lx
students (Luca Pistor) even got it running on the pi.
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