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486 points dbreunig | 1 comments | | HN request time: 0s | source
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isusmelj ◴[] No.41863460[source]
I think the results show that just in general the compute is not used well. That the CPU took 8.4ms and GPU took 3.2ms shows a very small gap. I'd expect more like 10x - 20x difference here. I'd assume that the onnxruntime might be the issue. I think some hardware vendors just release the compute units without shipping proper support yet. Let's see how fast that will change.

Also, people often mistake the reason for an NPU is "speed". That's not correct. The whole point of the NPU is rather to focus on low power consumption. To focus on speed you'd need to get rid of the memory bottleneck. Then you end up designing your own ASIC with it's own memory. The NPUs we see in most devices are part of the SoC around the CPU to offload AI computations. It would be interesting to run this benchmark in a infinite loop for the three devices (CPU, NPU, GPU) and measure power consumption. I'd expect the NPU to be lowest and also best in terms of "ops/watt"

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AlexandrB ◴[] No.41863552[source]
> Also, people often mistake the reason for an NPU is "speed". That's not correct. The whole point of the NPU is rather to focus on low power consumption.

I have a sneaking suspicion that the real real reason for an NPU is marketing. "Oh look, NVDA is worth $3.3T - let's make sure we stick some AI stuff in our products too."

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1. conradev ◴[] No.41866150[source]
The real consumers of the NPUs are the operating systems themselves. Google’s TPU and Apple’s ANE are used to power OS features like Apple’s Face ID and Google’s image enhancements.

We’re seeing these things in traditional PCs now because Microsoft has demanded it so that Microsoft can use it in Windows 11.

Any use by third party software is a lower priority