[joe_the_user]

Well, the success of neural nets over the past few years has come through harnessing massive processing power.

The problem is a lot of the programming can be low level and ad-hoc. I think the idea of the various DSLs is to allow the model to be compactly specified while having the programs go as fast as possible. A JIT may be one way to accomplish this.

[deepnotderp]

Relying upon a JIT often means the ability to create things which preclude the use of static compilers, which means that accelerated hardware, like ours, cannot be used efficiently.

[yorwba]

The kind of optimizations a static compiler might apply can be done by a JIT as well, with the added benefit of actually knowing what kind of workload is going to run. Most of deep learning is applying comparatively small computation graphs to very large arrays of numbers in parallel, so the overhead of compilation is only a small portion of the overall computation time. A smart JIT that decides on the optimal tiling pattern for the array dimensions observed at runtime and rewrites loops accordingly can easily pay for itself.

[Firadeoclus]

As a counterpoint (and not necessarily the one the GP is referring to), if you were compiling for, say, an FPGA the overhead of compilation would be very significant.

[deepnotderp]

Our processor is analogous to a CGRA, so compilation to it would indeed be hindered by a JIT based compiler.

[gugagore]

That's "course-grained reconfigurable architecture", for anyone else who didn't know.