fix typo

uwsampl · Nov 11, 2022 · d8d9ac4 · d8d9ac4
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diff --git a/README.md b/README.md
@@ -21,7 +21,7 @@ SparseTIR: Sparse Tensor Compiler for Deep Learning
 [Documentation](https://sampl.cs.washington.edu/sparsetir/) |
 [Paper](https://arxiv.org/abs/2207.04606)
 
-SparseTIR is a tensor-level compiler for sparse/irregular operators in Deep Learning. The design goal of SparseTIR is to provide a general programming abstraction that can cover both sparse and irregular (e.g. Ragged Tensors) sparse workloads in Deep Learning including Graph Neural Networks, Sparse Transformers, Sparse Convolutions, Network Pruning, etc. while generating high-performance code on heterogeneous hardware.
+SparseTIR is a tensor-level compiler for sparse/irregular operators in Deep Learning. The design goal of SparseTIR is to provide a general programming abstraction that can cover both sparse and irregular (e.g. Ragged Tensors) workloads in Deep Learning including Graph Neural Networks, Sparse Transformers, Sparse Convolutions, Network Pruning, etc. while generating high-performance code on heterogeneous hardware.
 
 The key innovation of SparseTIR is *composability*:
 - **Format Composability**: Decompose the computation on a single format to computation on hybrid formats.

diff --git a/docs/index.rst b/docs/index.rst
@@ -6,7 +6,7 @@
 Welcome to SparseTIR's documentation!
 =====================================
 
-SparseTIR is a tensor-level compiler for sparse/irregular operators in Deep Learning. The design goal of SparseTIR is to provide a general programming abstraction that can cover both sparse and irregular (e.g. Ragged Tensors) sparse workloads in Deep Learning including Graph Neural Networks, Sparse Transformers, Sparse Convolutions, Network Pruning, etc. while generating high-performance code on heterogeneous hardware.
+SparseTIR is a tensor-level compiler for sparse/irregular operators in Deep Learning. The design goal of SparseTIR is to provide a general programming abstraction that can cover both sparse and irregular (e.g. Ragged Tensors) workloads in Deep Learning including Graph Neural Networks, Sparse Transformers, Sparse Convolutions, Network Pruning, etc. while generating high-performance code on heterogeneous hardware.
 
 .. toctree::
    :maxdepth: 1