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README.md

@@ -48,7 +48,7 @@ print(x.grad)  # dz/dx
 print(y.grad)  # dz/dy
 ```
 
-### Neural networks?
+## Neural networks?
 
 It turns out, a decent autograd tensor library is 90% of what you need for neural networks. Add an optimizer (SGD, RMSprop, and Adam implemented) from tinygrad.optim, write some boilerplate minibatching code, and you have all you need.
 
@@ -78,7 +78,7 @@ loss.backward()
 optim.step()
 ```
 
-### GPU Support?!
+## GPU Support
 
 tinygrad supports GPUs through PyOpenCL.
 
@@ -87,7 +87,7 @@ from tinygrad.tensor import Tensor
 (Tensor.ones(4,4).cuda() + Tensor.ones(4,4).cuda()).cpu()
 ```
 
-### ANE Support?!?!
+### ANE Support?!
 
 If all you want to do is ReLU, you are in luck! You can do very fast ReLU (at least 30 MEGAReLUs/sec confirmed)
 
@@ -103,7 +103,18 @@ print(b.cpu())
 
 Warning: do not rely on the ANE port. It segfaults sometimes. So if you were doing something important with tinygrad and wanted to use the ANE, you might have a bad time.
 
-### ImageNet inference
+### Adding an accelerator
+
+You need to support 14 basic ops:
+
+```
+Add, Sub, Mul, Pow, Sum, Dot
+Pad2D, Reshape
+Relu, Sigmoid, LogSoftmax
+Conv2D, MaxPool2D, AvgPool2D
+```
+
+## ImageNet inference
 
 Despite being tiny, tinygrad supports the full EfficientNet. Pass in a picture to discover what it is.
 
@@ -129,7 +140,7 @@ See `examples/mnist_gan.py`
   <img src="https://raw.githubusercontent.com/geohot/tinygrad/master/docs/mnist_by_tinygrad.jpg">
 </p>
 
-### The promise of small
+## The promise of small
 
 tinygrad will always be below 1000 lines. If it isn't, we will revert commits until tinygrad becomes smaller.
 
@@ -142,9 +153,6 @@ python3 -m pytest
 ### TODO
 
 * Train an EfficientNet on ImageNet
-  * Make broadcasting work on the backward pass (simple please)
-  * EfficientNet backward pass
-  * Tensors on GPU (a few more backward)
 * Add a language model. BERT?
 * Add a detection model. EfficientDet?
 * Reduce code