Update README.md

README.md

 # Deep Graph Library (DGL)
 
+[![PyPi Latest Release](https://img.shields.io/pypi/v/dgl.svg)](https://pypi.org/project/dgl/)
+[![Conda Latest Release](https://anaconda.org/dglteam/dgl/badges/version.svg)](https://anaconda.org/dglteam/dgl)
 [![Build Status](http://ci.dgl.ai:80/buildStatus/icon?job=DGL/master)](http://ci.dgl.ai:80/job/DGL/job/master/)
+[![Benchmark by ASV](http://img.shields.io/badge/benchmarked%20by-asv-green.svg?style=flat)](https://asv.dgl.ai/)
 [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](./LICENSE)
 
 [Documentation](https://docs.dgl.ai) | [DGL at a glance](https://docs.dgl.ai/tutorials/basics/1_first.html#sphx-glr-tutorials-basics-1-first-py) | [Model Tutorials](https://docs.dgl.ai/tutorials/models/index.html) | [Discussion Forum](https://discuss.dgl.ai)
@@ -89,7 +92,7 @@ class GATLayer(nn.Module):
 ```
 ## Performance and Scalability
 
-**Microbenchmark on speed and memory usage**: While leaving tensor and autograd functions to backend frameworks (e.g. PyTorch, MXNet, and TensorFlow), DGL aggressively optimizes storage and computation with its own kernels. Here's a comparison to another popular package -- PyG. The short story is that raw speed is similar, but DGL has much better memory management.
+**Microbenchmark on speed and memory usage**: While leaving tensor and autograd functions to backend frameworks (e.g. PyTorch, MXNet, and TensorFlow), DGL aggressively optimizes storage and computation with its own kernels. Here's a comparison to another popular package -- PyTorch Geometric (PyG). The short story is that raw speed is similar, but DGL has much better memory management.
 
 
 | Dataset  |    Model     |                   Accuracy                   |                    Time <br> PyG &emsp;&emsp; DGL                    |           Memory <br> PyG &emsp;&emsp; DGL            |