Excessive memory usage on ARM
Nobody has claimed this yet.
- Dominant language
- C++
- Stars
- 23.8k
- Forks
- 4.5k
- Avg merge
- 2d 20h
- Merged PRs (30d)
- 37
Description
detail | 详细描述 | 詳細な説明
Hi,
I'm experiencing some really bad memory usage issues on running ncnn on ARM devices (Android and iOS).
The model is only 65.4MB big, but right after loading params and bin on mobile, the memory usage grows up to 400-500MB (cannot give you exact number because Android Studio profiler is shitty).
In the past I used the same model with TFLite and MLCore and I never noticed this memory usage.
Is there any workaround to decrease memory usage on ARM? I tried messing with opt.use_packing_layout and opt.use_int8_packed options, but nothing helped so far.
Please let me know if I'm missing something, is this memory usage expected?
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reproducing the reported memory increase when loading the model's params and bin files on an ARM Android or iOS device, using a reliable profiler. Compare the behavior with opt.use_packing_layout and opt.use_int8_packed enabled and disabled; done means establishing whether the usage is expected or documenting a verified workaround.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- android, cpp, ios
- Domain
- machine-learning, mobile-dev, performance
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100