
TensorFlow can now run on £12 edge hardware - TecheratiJames
https://techerati.com/news-hub/tensorflow-can-now-run-on-12-edge-hardware/
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scottlamb
"Cortex M4 processor is extremely low power, using less than 1 MW in many
cases"

Talk about low standards. Data centers run into megawatts but I expect more
power efficiency from embedded processors. (Why do articles so frequently mix
up milli (m) and mega (M) like this? I get that it's just capitalization but
they teach this in science classes, right?)

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jrockway
It's probably a typo. Easy to make when you've just been saying M4 a few words
earlier.

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ben_w
Perhaps. Articles like this also frequently say “PC and MAC”.

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jrockway
Ah yes, Media Access Control is my favorite operating system.

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dvfjsdhgfv
You never know, maybe they meant Mandatory Access Control.

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avmich
Medium Attachment Cable.

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westmeal
MAC and Cheese

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dafrankenstein2
"the model takes up only 20KB of flash storage space, the footprint of the
TensorFlow Lite code just 25KB, and the app itself only 30KB of RAM."

Those guys on google and outside google surely worked hard to achieve this
level of benchmark!

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DerDangDerDang
Would be interested to hear how the most recent TFlite stacks up against ArmNN
running the same model. ArmNN will run TF, TFlite, or caffe models and is
built (by Arm) for speed on Arm hardware

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antpls
Benchmarks about mobile NN frameworks : [https://github.com/XiaoMi/mobile-ai-
bench](https://github.com/XiaoMi/mobile-ai-bench) , not sure it contains
ArmNN, but it's a start and you could open an issue about it maybe

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dvfjsdhgfv
Actually you don't need the $15 Sparkfun Edge as STM32F103 boards can also be
used [0], and you can have them for $3 (probably even less).

[0]
[https://github.com/google/stm32_bare_lib](https://github.com/google/stm32_bare_lib)

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nerfhammer
the F means it has native floating point, right?

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05
Nope, STM32F3 family and higher has it, though (Cortex-M4f)

[https://en.m.wikipedia.org/wiki/STM32#Overview](https://en.m.wikipedia.org/wiki/STM32#Overview)

~~~
nerfhammer
They got tensorflow working without floating point!?

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gambler
If you're interested in bringing deep learning to edge hardware, I'd suggest
looking at XNOR nets:

[https://allenai.org/plato/xnornet/](https://allenai.org/plato/xnornet/)

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nmstoker
Here's Pete demonstrating / talking about it too:
[https://youtu.be/bDZ2q6OktQI?t=6922](https://youtu.be/bDZ2q6OktQI?t=6922)

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nerfhammer
The dev board:
[https://www.sparkfun.com/products/15170](https://www.sparkfun.com/products/15170)

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amelius
How long would it take one of these boards to run e.g. Google's Inception v4
network?

In other words, how would this hardware compare in these benchmarks:
[https://www.tensorflow.org/lite/performance/benchmarks](https://www.tensorflow.org/lite/performance/benchmarks)

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mr_toad
Title should really say TensorFlow _Lite_.

Training a model on £12 hardware would be really something.

