[mlir][sparse] update bibliography of sparse tensor dialect

mlir/include/mlir/Dialect/SparseTensor/IR/SparseTensorBase.td

let description = [{
 
     The concept of **treating sparsity as a property, not a tedious
     implementation detail**, by letting a **sparse compiler** generate
     sparse code automatically was pioneered for linear algebra by [Bik96]
     in MT1 (see https://www.aartbik.com/sparse.php) and formalized
     to tensor algebra by [Kjolstad17,Kjolstad20] in the Sparse Tensor
     Algebra Compiler (TACO) project (see http://tensor-compiler.org).
 
-    The MLIR implementation closely follows the "sparse iteration theory"
-    that forms the foundation of TACO. A rewriting rule is applied to each
-    tensor expression in the Linalg dialect (MLIR's tensor index notation)
+    The MLIR implementation [Biketal22] closely follows the "sparse iteration
+    theory" that forms the foundation of TACO. A rewriting rule is applied to
+    each tensor expression in the Linalg dialect (MLIR's tensor index notation)
     where the sparsity of tensors is indicated using the per-dimension level
     types dense/compressed together with a specification of the order on the
     dimensions (see [Chou18] for an in-depth discussions and possible
     extensions to these level types). Subsequently, a topologically sorted
     iteration graph, reflecting the required order on indices with respect
     to the dimensions of each tensor, is constructed to ensure that all tensors
     are visited in natural index order. Next, iteration lattices are
     constructed for the tensor expression for every index in topological
     order. Each iteration lattice point consists of a conjunction of tensor
     indices together with a tensor (sub)expression that needs to be evaluated
     for that conjunction.  Within the lattice, iteration points are ordered
     according to the way indices are exhausted. As such these iteration
     lattices drive actual sparse code generation, which consists of a
     relatively straightforward one-to-one mapping from iteration lattices
     to combinations of for-loops, while-loops, and if-statements. Sparse
     tensor outputs that materialize uninitialized are handled with
     insertions in pure lexicographical index order if all parallel loops
     are outermost or using a 1-dimensional access pattern expansion
     (a.k.a. workspace) where feasible [Gustavson72,Bik96,Kjolstad19].
 
     * [Bik96] Aart J.C. Bik. Compiler Support for Sparse Matrix Computations.
     PhD thesis, Leiden University, May 1996.
+    * [Biketal22] Aart J.C. Bik, Penporn Koanantakool, Tatiana Shpeisman,
+    Nicolas Vasilache, Bixia Zheng, and Fredrik Kjolstad. Compiler Support
+    for Sparse Tensor Computations in MLIR. ACM Transactions on Architecture
+    and Code Optimization, June, 2022 (see https://dl.acm.org/doi/10.1145/3544559).
     * [Chou18] Stephen Chou, Fredrik Berg Kjolstad, and Saman Amarasinghe.
     Format Abstraction for Sparse Tensor Algebra Compilers. Proceedings of
     the ACM on Programming Languages, October 2018.
     * [Gustavson72] Fred G. Gustavson. Some basic techniques for solving
     sparse systems of linear equations. In Sparse Matrices and Their
     Applications, pages 41–52. Plenum Press, New York, 1972.
     * [Kjolstad17] Fredrik Berg Kjolstad, Shoaib Ashraf Kamil, Stephen Chou, David
     Lugato, and Saman Amarasinghe. The Tensor Algebra Compiler. Proceedings of