Updates Readme to add GroupNorm (#365)

## Summary
Updates the Readme to show that we now support GroupNorm and the
potential speedup that can be achieved.

## Testing Done
n/a

- Hardware Type: <BLANK>
- [X] run `make test` to ensure correctness
- [X] run `make checkstyle` to ensure code style
- [X] run `make test-convergence` to ensure convergence

README.md

@@ -273,6 +273,7 @@ loss.backward()
 
 - **RMSNorm**: [RMSNorm](https://arxiv.org/pdf/1910.07467), which normalizes activations using their root mean square, is implemented by fusing the normalization and scaling steps into a single Triton kernel, and achieves ~3X speedup with ~3X peak memory reduction.
 - **LayerNorm**: [LayerNorm](https://arxiv.org/pdf/1607.06450), which centers and normalizes activations across the feature dimension, is implemented by fusing the centering, normalization and scaling steps into a single Triton kernel, and achieves ~2X speedup.
+- **GroupNorm**: [GroupNorm](https://arxiv.org/pdf/1803.08494), which normalizes activations across the group dimension for a given sample. Channels are grouped in K groups over which the normalization is performed, is implemented by fusing the centering, normalization and scaling steps into a single Triton kernel, and can achieve up to ~2X speedup as the number of channels/groups increases.
 - **RoPE**: [Rotary Positional Embedding](https://arxiv.org/pdf/2104.09864) is implemented by fusing the query and key embedding rotary into a single kernel with inplace replacement, and achieves ~3X speedup with ~3X peak memory reduction.
 - **SwiGLU**: [Swish Gated Linear Units](https://arxiv.org/pdf/2002.05202), given by
 $$\text{SwiGLU}(x)=\text{Swish}_{\beta}(xW+b)\otimes(xV+c)$$