Generally, no. The IR will be tuned for compiler purposes, not execution purposes. It can be possible but it won't be optimal. For instance, the IR will probably retain type information that generally CPUs don't care about, since they live in a world of bits. The IR will have been designed knowing it is upstream of optimization code, so it won't be something that is already optimal, it'll be something designed to be easily made optimal in many different ways.

As Blackthorn mentioned, specifically it can be done. There was a chip that ran JVM bytecode directly. There was an entire Lisp Machine which was very influential... but not because of its high performance.

Wikipedia has a page on this: https://en.wikipedia.org/wiki/High-level_language_computer_a...

Many people decry the lack of innovation in computer architecture over the past 50-60 years. There is a legitimate reason for that, though: Any such innovation had to outrun the exponential increases conventional architecture was experiencing. We had it for so long we could take it for granted, but while computing wasn't the first and won't be the last to see periods of exponential growth, I can't think of anything else that had the length of run that computer chips had. (And even now, it's only slowed down. There's still multiplicative advances rather than additive ones every year. The base on the exponent is just smaller than it used to be.) There's been a number of architectures based on running IR (or an equivalent directly) that didn't pan out, but there's also some that did run OK, but they just couldn't keep up with the exponentially accelerating behemoth.

I think if silicon continues to plateau, there is some hope that this could change. However, a new challenger has appeared! Now it's not good enough to just outrun what a "conventional" architecture can do with CPU and RAM and peripherals... now you also need outrun what a GPU-based architecture can do! This sucks up a lot of the oxygen. For instance, right now you see some tentative steps towards special-purpose AI silicon, but it has to do something amazing to beat out a GPU, and you also have to be really, really sure that the AI technique you are committing to silicon will still be the state-of-the-art in the couple of years it'll take you to bring it to market. AI hasn't been moving as fast as, say, JS front-end frameworks, but it's still moving fast enough that I'd be nervous in investing in that vs. the same amount of effort poured into optimizing a GPU algorithm on GPUs that are still advancing a lot every year.