
Bringing neural networks to cellphones - jonbaer
http://news.mit.edu/2017/bringing-neural-networks-cellphones-0718
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bwasti
I doubt pruning alone is going to serve up the same kind of wins that energy
aware architectures will. The main problem is that convolutions are heavy
computational operations. There has been ongoing research in modifications to
the convolution that basically decrease the amount of computation being done
in channel space (grouped and depthwise separable convolutions). The tradeoff
is accuracy, but new techniques (like shuffling the channels) are helping
recover that.

MobileNets and ShuffleNet are some examples. They see 10x reductions in MACs,
far more than the "73 percent reduction in power consumption over the standard
implementation of neural networks" talked about in this article.
[https://arxiv.org/pdf/1704.04861.pdf](https://arxiv.org/pdf/1704.04861.pdf)
[https://arxiv.org/pdf/1707.01083.pdf](https://arxiv.org/pdf/1707.01083.pdf)

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bmc7505
Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware
Pruning:
[https://arxiv.org/pdf/1611.05128.pdf](https://arxiv.org/pdf/1611.05128.pdf)

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JPLeRouzic
There are already providers of building blocks for the fabless SoC industry,
that use various NN schemes. For example to better detect signal in noise like
in iterative codes [0, 1].

I remember a meeting with such a company, that offered building blocks to
classify signals, but unfortunately I do not remember its name.

[0]
[http://ieeexplore.ieee.org/document/1180574/](http://ieeexplore.ieee.org/document/1180574/)

[1]
[http://ieeexplore.ieee.org/document/1618080/](http://ieeexplore.ieee.org/document/1618080/)

