Author ported their software between near-identical AMD AIE and NPU platforms, https://www.hackster.io/tina/tina-running-non-nn-algorithms-...

> The PFB is found in many different application domains such as radio astronomy, wireless communication, radar, ultrasound imaging and quantum computing.. the authors worked on the evaluation of a PFB on the AIE.. [developing] a performant dataflow implementation.. which made us curious about the AMD Ryzen NPU.

> The [NPU] PFB figure shows.. speedup of circa 9.5x compared to the Ryzen CPU.. TINA allows running a non-NN algorithm on the NPU with just two extra operations or approximately 20 lines of added code.. on [Nvidia] GPUs CUDA memory is a limiting factor.. This limitation is alleviated on the AMD Ryzen NPU since it shares the same memory with the CPU providing up to 64GB of memory.

Consumer Ryzen NPU hardware is more accessible to students and hackers than industrial Versal AIE products.

The Versal AI Engine is the NPU. And the Ryzen CPUs NPU is almost exactly a Versal AI Engine IP block to the point that in the Linux kernel they share the same driver (amdxdna) and the reference material the kernel docs link to for the Ryzen NPUs is the Versal SoC's AI Engine architecture reference manual

https://docs.kernel.org/next/accel/amdxdna/amdnpu.html

My issue with your comment is that you're acting as if you're clarifying something, but you're just replacing it with another confusion.

There are three generations of AI Engines: AIE, AIE-ML and AIE-MLv2.

The latter are known as XDNA and XDNA2, which are available on laptops and the 8000G series on desktops. The former is exclusively available on select FPGAs specialising in DSP using single precision floating point.

The AI focused FPGAs use AIE-MLv2 and therefore are identical to XDNA2.