Thanks! I'm trying my best!

Yep, you're right! I'm just using NPU TOPS since this video is only about Neural Processing Units.

Not exactly, why do you think to do something with just 1-2W power consumption is worth less like to do that with 300-400W power consumption..? NPU only can do AI things, but it's efficient in that, only reason why it's not worth to stack it infinitely, because it's can't do anything else, meanwhile the GPU can do graphical calculations when it's not doing AI thigs..

@@TamasKiss-yk4st A GPU uses 300-400W if it's specifically the 4090, and doing heavy operations. I can sense the "Pulled out of my ass" already.

And what if I tell you, that the NPU engineers can put a SRAM/DRAM inside the chip and with the proper NN compiler software, they can reduce the usage of external memory? Also, the main reasons to develop NPU-s are the cases when you have to deal with Power-Performance questions. Think about self-driving solutions: every manufacturer is going to choose an NPU over any GPU, because as long as most of Nvidia GPUs does not care about the power consumption (and the latest cards using 200-300 W), a well-built NPU could require only 20-30 W for the same result. Some explanation about same result: most modern neural networks use a lot of complex math operations with large GMAC requirement inside of them. If you build a hardware that can help the parallelisation of the exact operation, you will need less TOPS to execute it as you run it on a GPU. (GPUs can solve matrix multiplications efficiently, so it’s compiler tries to convert everything to it and optimize it with various kinds of algorithms. But in the end you replaced an operation with a more “expensive” operation. )

Yeah, but the NPU itself is delivering 45+ TOPS.