能否介绍下NCNN的提升计算速度的一些方法？
Open
Nobody has claimed this yet.
- Dominant language
- C++
- Stars
- 23.8k
- Forks
- 4.5k
- Avg merge
- 2d 20h
- Merged PRs (30d)
- 37
Description
目前看NCNN确实要比Caffe等框架要快的多，希望 @nihui 可以介绍一些。
然后X86下的卷积计算方法我没太看懂，能否稍微介绍解释下如何实现的？
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 reading the NCNN x86 convolution implementation and the repository topics on SIMD, ARM NEON, and Vulkan. Document the main methods used to improve inference speed and explain how the x86 convolution approach works. Done means the explanation is understandable without requiring the reader to infer the implementation details.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- cpp
- Domain
- machine-learning, performance
- Issue type
- Documentation
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100