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> the 45 trillion operations per second that’s listed in the specs Such a spec should be ideally be accompanied by code demonstrating or approximating the claimed performance. I can't imagine a sports car advertising a 0-100km/h spec of 2.0 seconds where a user is unable to get below 5 seconds.
> the 45 trillion operations per second that’s listed in the specs Such a spec should be ideally be accompanied by code demonstrating or approximating the claimed performance. I can't imagine a sports car advertising a 0-100km/h spec of 2.0 seconds where a user is unable to get below 5 seconds.
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"
I know Apple's Neural Engine is used to power Face ID and the facial recognition stuff in Photos, among other things.
It seems like the NPUs are for very optimized models that do small tasks, like eye contact, background blur, autocorrect models, transcription, and OCR. In particular, on Windows, I assumed they were running the full screen OCR (and maybe embeddings for search) for the rewind feature.
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…
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."
The write up on the GitHub repo is much more informative than the blog. When running int8 matmul using onnx performance is ~0.6TF. https://github.com/usefulsensors/qc_npu_benchmark
In general MAC unit utilization tends to be low for transformers, but 1.3% seems pretty bad. I wonder if they fucked up the memory interface for the NPU. All the MACs in the world are useless if you cannot feed them.
I thought the purpose of these things was not to be fast, but to be able to run small models with very little power usage? I have a newer AMD laptop with an NPU, and my power usage doesn't change using the video effects that supposedly run on it, but goes up when using the nvidia studio effects. It seems like the NPUs are for very optimized models that do small tasks, like eye contact, background blur, autocorrect mo…
You can see this in action when evaluating a CoreML model on a macOS machine. The ANE takes half as long as the GPU which takes half as long as the CPU (actual factors being model dependent)
In general MAC unit utilization tends to be low for transformers, but 1.3% seems pretty bad. I wonder if they fucked up the memory interface for the NPU. All the MACs in the world are useless if you cannot feed them.
It's a tablet. It probably has like one DDR channel. It's not so much that they "fucked it up" as that they knowingly built a grossly unbalanced system so they could report a pointless number.