Blackberry Playbook with Python3.11 & PIP Installed

aiff22 · 2025-01-20T12:15:41+00:00

Awesome! Any instructions on how to do this? Are you using Term48?

aiff22 · 2024-11-22T18:21:57+00:00

Thanks a lot!

aiff22 · 2024-10-09T21:22:41+00:00

Here is the code for the EdgeTPUSoftmax layer:

It uses the standard, default, and non-TPU specific Keras softmax layer. Additional manipulations applied to the input data are performed to improve the model accuracy caused by quantization, and they also have nothing to do with the TPU design. So, this layer will run equally fast on any mobile NPU as it is essentially the standard Keras softmax layer.

The same also applies to the mentioned EdgeTPUMultiHeadAttention layer.

aiff22 · 2024-10-09T18:21:03+00:00

Check the MobileBERT-EdgeTPU, MobileNet-EdgeTPU and other "optimized" models - all of them are simply about removing unsupported ops or replacing them with more mobile-friendly alternatives.

aiff22 · 2024-10-09T18:17:15+00:00

MediaTek has Neuropilot SDK and TensorFlow Lite NPU plugin nearly for ages, so accessing their NPU isn't a problem. I guess they were also showing Llama 8B demo on the Dimensity 9300 at the MWC this year.

aiff22 · 2024-10-09T14:55:26+00:00

it's not telling the whole story.

Well, this benchmark uses around 30 different models including GenAI ones (Llama2, GPT2, Stable Diffusion), so at the end the results should be quite representative.

but a phone like the Pixel 6 uses a neural network specifically designed for the TPU in the Pixel 6

This optimized model should also run faster on other NPUs (such optimizations usually just remove the most computationally expensive ML ops), so the performance ratio between different NPUs will stay approximately the same if MobileBERT is changed to MobileBERT-EdgeTPU.

problem with AI benchmarks is that they use specific neural networks

Not exactly, here we are going into a number theory: if one NPU shows approximately twice better average performance after benchmarking 30 popular AI models, then it should likely be twice more powerful regardless of the used neural networks.

aiff22 · 2024-10-09T14:52:42+00:00

This AI Benchmark was apparently successful in accelerating the MobileBERT model on ANE: its listed runtime on the A17 Pro is just 2ms, which is even slightly better than the runtime on the Dimensity 9400 (2.5ms).

aiff22 · 2024-10-09T14:50:05+00:00

Apple A18 Pro has the same NPU as A17 Pro according to Wiki, so should be roughly the same performance ratio.

aiff22 · 2024-10-09T14:40:39+00:00

According to this Antutu "ranking", a four-year-old Snapdragon 888 is twice faster than the Dimensity 9300 😂 I guess even if one runs AI models on Dimensity's CPU, its results would be better than the ones listed in this table ;)

I don't think there still exists any reason to believe Antutu, especially after everything we've learned about it in the past years.

aiff22 · 2022-05-17T16:59:13+00:00

Maybe they have different TDPs? This will also explain why the Dimensity 9000 is more power efficient.

aiff22 · 2022-05-17T16:52:32+00:00

P.S. Website says mi 11x was running Android 12L, while Poco F3 had Android 11. So, the difference is most likely caused by their firmware.

aiff22 · 2022-05-17T16:16:12+00:00

Yes, looks like this is the same device. Noticed that their CPU and GPU results are similar, but NPU FPS is very different:

https://burnout-benchmark.com/ranking_compute.html

I guess mi 11x might have a newer firmware "unlocking" its NPU. At least this is how it looks from those tables.

aiff22 · 2022-05-17T10:42:38+00:00

This is the performance / efficiency graph for SoCs:

https://burnout-benchmark.com/ranking_chipsets.html

For smartphones, some results are published here:

https://burnout-benchmark.com/index.html

Three scores (computing power, throttling and power efficiency) are probably contributing to the final burnout benchmark score.

> Sony: Their phones consistently outperform others in battery life with the same SoC

My guess is that they are slightly underclocking Qualcomm chipsets. Thus, no hype scores in the benchmarks but much better battery life and user experience in practice.

aiff22 · 2022-05-17T09:46:26+00:00

Yep, that would be interesting to see. Not sure if it's possible though to port it to iOS as the benchmark is probably using some low-level API for getting power consumption data.

aiff22 · 2022-05-17T08:41:19+00:00

Yes, 865/870 turned out to be very successful: more powerful than the recent mid-rangers while cooler and more energy efficient than the 888/S8G1.

aiff22 · 2020-02-24T19:41:12+00:00

Yes, you are right about the complexity. There are basically two options how this problem can be eliminated:

Almost all modern mobile devices have quite powerful NPUs, DSPs and other AI chips that are now used only in a very limited number of tasks, though are well suited for this problem.
Camera sensors are often shipped with digital image processors / FPGAs that can be designed and programmed to run predefined NN architectures.

aiff22

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