As I was investigating the potential usefulness of TVM as a high-level behavioral simulator for a new AI accelerator architecture, the tvm.relay.backend.interpreter.Interpreter
class caught my eye.
Its optimize()
function appears to be the first (only?) step in moving a generic computation graph towards a particular compute architecture.
I think I know how to generate its mod
argument from a TensorFlow design, using the relay.frontend.from_tensorflow()
function.
But, how do I create its ctx
and target
arguments?
I’m assuming that both of those should be customized to reflect the nature of my new architecture; is that correct?
Is there a tutorial for this available somewhere?
Thanks!