What do you need Matrix Cores for when you already have a NPU which can access the same memory, and even seems to include more flexible FPGA fabric? It's six of one, half a dozen of another.
What do you need Matrix Cores for when you already have a NPU which can access the same memory, and even seems to include more flexible FPGA fabric? It's six of one, half a dozen of another.
I have the HP Zbook G1a running the same CPU and RAM under HP Ubuntu. I have not seen any OOTB way to use the TPU. I can get ROCm software to run but it does not use it. No system tools show its activity that I can see. It seems to be a marketing gimmick. Shame.
https://news.ycombinator.com/item?id=43671940#43674311
> 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.
The NPU is generally pretty weak and not pipelined into the GPU's logic (which is already quite large on-die). It feels like the past 10 years have taught us that if you're going to create tensor-specific hardware then it makes the most sense to put it in your GPU and not a dark-silicon coprocessor.
Can you do GPU -> NPU -> GPU for streaming workloads? The GPU can be more flexible than Tensor HW for preprocessing, light branching, etc.
Also, Strix Halo NPU is 50 TOPS. The desktop RDNA 4 chips are into the 100s.
As for consumer uses, I mentioned it's an open question. Blender? FFmpeg? Database queries? Audio?