Update README.md to include v3.0 Ruby description.

README.md

@@ -2,9 +2,9 @@
 
 ## About
 
-Timeloop is an infrastructure that aims to provide modeling, mapping and code-generation for Explicitly-Decoupled Data Orchestration (EDDO) architectures, with a focus on for dense- and sparse- tensor algebra workloads. It is built from two modular components:
+Timeloop is an infrastructure that aims to provide modeling, mapping and code-generation for dense- and sparse- tensor algebra workloads on a range of accelerator architectures. It is built from two modular components:
 
-* A fast analytical model that can emulate a range of EDDO architecture designs and provide performance and energy projections
+* A fast analytical model that can emulate a range of architecture designs and provide performance and energy projections
 * A mapper that that searches for an optimal mapping in the space of mappings of a tensor-algebra problem on a given architecture
 
 ## Documentation
@@ -14,6 +14,8 @@ For a deeper understanding of Timeloop's internals please read our [ISPASS 2019
 
 Timeloop version 2.0 (a.k.a. Sparseloop) provides stochastic modeling of compressed-sparse tensor algebra. This work is described in our [MICRO 2022 paper](https://www.computer.org/csdl/proceedings-article/micro/2022/627200b377/1HMSE23T13a).
 
+Timeloop version 3.0 (a.k.a. Ruby) adds support for imperfectly-factorized mappings (described in our [ISPASS 2022 paper](https://ieeexplore.ieee.org/document/9804679)), in addition to support for spatial skews and flattened mappings.
+
 ## Tutorial
 
 New users are strongly encouraged to complete the Timeloop [tutorial](https://accelergy.mit.edu/tutorial.html). Serially walking through the [exercises](https://github.com/Accelergy-Project/timeloop-accelergy-exercises/) from the tutorial serves as an essential hands-on introduction to the tool.