Hello,
Currnetly I am developing DL4J models for Android applications. My models must be very light because all computations are done on CPU. Do you have any plans to make somekind mobile GPU support? I think that this option is very important.
Thanks on your time,
@lavajaw yeah we’re working on that. We need to finish up our ARM additions:
KonduitAI:master ← KonduitAI:qwr_armcompute
opened 02:58PM - 09 Jul 20 UTC
## What changes were proposed in this pull request?
Ops Additions:
- added con… v2d (float32)
- added deconv2d (float32)
- minor fixes of coding-style and so on
libnd4j cross-compilation script for pi(arm):
- use new arm cross compiler from the official arm site
- minor fixes in CmakeLists
- fix DEBUG compilation by manually switching linker to // ld.gold --long-plt //, because of faulty gcc -fuse-ld=gold switch
ShapeDescriptor:
- validation
- paddedBuffer helper
- proper allocLength ~~and other helper member methods~~
- the flag indicating that Array having padded buffer
NdArray Factory methods
- generic flexible interface for creating NdArray
- generic flexible interface for padded NdArrays. As it is more generic it's easier to simulate arm_compute {top, right, bottom, left} padding with it.
## How was this patch tested?
unit tests
## Quick checklist
The following checklist helps ensure your PR is complete:
- [x] Eclipse Contributor Agreement signed, and signed commits - see [IP Requirements](https://deeplearning4j.org/eclipse-contributors) page for details
- [x] Reviewed the [Contributing Guidelines](https://github.com/eclipse/deeplearning4j/blob/master/CONTRIBUTING.md) and followed the steps within.
- [x] Created tests for any significant new code additions.
- [x] Relevant tests for your changes are passing.
We put that on hold a bit but will be picking it back up soonish.
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