|Re: [eigen] stability of internal "packet math" interfaces|
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- To: eigen@xxxxxxxxxxxxxxxxxxx
- Subject: Re: [eigen] stability of internal "packet math" interfaces
- From: Benoit Steiner <benoit.steiner.goog@xxxxxxxxx>
- Date: Wed, 13 Jan 2016 20:10:07 -0800
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There's been a fair amount of work put in Eigen recently to add support for the functions most commonly used in neural networks. For example, tanh and erf are now part of the public API. I would encourage you to use these directly in your implementation, and contribute back performance improvements that you may have.
We've been using the internal packet apis for about 2 years now, and they've been very stable. The only change that I can remember is that they've been extended with new functions.
We often use the EIGEN_DECLARE_CONST_Packet4f macros to instantiate constant packets. That seems to work well. Maybe you could try to use them and see if that helps?