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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.

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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.

Don't forget that AMD also bought Xilinx

@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.

How do you expect FPGAs to be useful here?

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