The main difference between Legion and TensorFlow is how and when the dataflow graph is constructed. In TensorFlow the graph is constructed lazily (no execution is performed until you've asked for it), it's optimized, and then distributed to processors (GPUs/TPUs) for execution. In Legion, the graph is built, distributed, and executed on the fly. What this means is that Legion can react to things like dynamic control flow (e.g. branches inside of loops) and analyze dependences at runtime to find task parallelism, in a similar way to how your out-of-order CPU extracts instruction level parallelism from a program. Doing things in the TensorFlow model works better when you can see your whole program up front and can "statically" optimize and schedule it because it has lower overheads, but it also has limits to the kinds of programs it can handle; the Legion approach works better when you have dynamic data-dependent behavior in your program and you need to react to it on the fly, but it does have some overhead to the dynamic analysis.

Check out https://news.ycombinator.com/item?id=20161902

TL;DR is TF executor parallelism is too pessimistic to fully exploit the parallelization opportunities in the problem space. Regent is built on top of Legion which is a cutting edge dataflow library, and is designed to provide the guarantees to achieve that speedup.