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use tinynn for Conv2d

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George Hotz
2021-10-30 19:40:44 -07:00
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README.md
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@@ -12,9 +12,9 @@ This may not be the best deep learning framework, but it is a deep learning fram
The core of it is in `tinygrad/`
Due to its extreme simplicity, it aims to be the easiest framework to add new accelerators to, with support for both inference and training. Support the simple basic ops, and you get SOTA [vision](https://arxiv.org/abs/1905.11946) `models/efficientnet.py` and [language](https://arxiv.org/abs/1706.03762) `models/transformer.py` models. We are working on support for the Apple Neural Engine.
Due to its extreme simplicity, it aims to be the easiest framework to add new accelerators to, with support for both inference and training. Support the simple basic ops, and you get SOTA [vision](https://arxiv.org/abs/1905.11946) `models/efficientnet.py` and [language](https://arxiv.org/abs/1706.03762) `models/transformer.py` models.
Eventually, [we will build custom hardware](https://geohot.github.io/blog/jekyll/update/2021/06/13/a-breakdown-of-ai-chip-companies.html) for tinygrad, and it will be blindingly fast. Now, it is slow.
We are working on support for the Apple Neural Engine and the Google TPU in the `accel/` folder. Eventually, [we will build custom hardware](https://geohot.github.io/blog/jekyll/update/2021/06/13/a-breakdown-of-ai-chip-companies.html) for tinygrad, and it will be blindingly fast. Now, it is slow.
### Installation
@@ -94,7 +94,7 @@ from tinygrad.tensor import Tensor
(Tensor.ones(4,4).gpu() + Tensor.ones(4,4).gpu()).cpu()
```
### ANE Support?!
### ANE Support?! (broken)
If all you want to do is ReLU, you are in luck! You can do very fast ReLU (at least 30 MEGAReLUs/sec confirmed)