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Comment by evancox100 | Hacker News Reader

Re. The “hybrid algorithms” bit: I was at this talk. The example she gave was a physics sim like CFD, iterating between a fast/approximate ML-based algorithm and slow/accurate classical physics algorithm, with the output of each feeding in as the starting point of the next round. But this was just an example, clearly there are lots of area you could apply a similar approach.

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The main thing AMD has in their accelerators that enables this this is unified memory between CPU and GPU. Thats really interesting.

Don't forget that AMD also bought Xilinx

How do you expect FPGAs to be useful here?

@WithinReason

I expect them to use Xilinix's AI engines primarily in their CPUs, APUs and GPUs - not so much FPGA.

I expect Xilinx's AI engines to never be integrated into anything AMD. Because Xilinx AI engines are VLIW - SIMD machines running their own instruction set.

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AMD is doing the right thing with Xilinx tech: they're integrating it into ROCm, so that Xilinx AI engines / FPGAs can interact with CPUs and GPUs. But there's no reason why these "internal core bits" should be shared between CPU, GPU, and FPGA.

This has been something I've been incredibly pleased with on the apple silicon SOC's. Albeit slowly, being able to load large datasets or blender scenes on a portable, efficient laptop and still being able to use the GPU is a nice touch.

Of course performance wise it doesn't touch the $1k+ graphics cards with crazy amounts of ram, but for students and if I need to do something quick on the go, its a really useful tool.

I think its also leaning into their new product MI300, which has 24 cpu cores with 8 compute dies. Both CPU and GPU (and memory) on a single package.

Conventional processing + AI together. Hybrid approaches.

Yes they were really crowing about MI250X adoption and touting the upcoming MI300

Afaik, having worked in HPC, one area where this can be employed is error bias correction of CFD models. E.g. weather models have various biases that need to be corrected for - so far this is just done with some relatively simple statistics afaik.

So differentiable programming with nns as approximators? Cool!

I imagine something like AlphaZero would also benefit: It's basically a hybrid of tree search and neural network.

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