Before revision:  The extensions include explicit communication, synchronization, and mapping buffers to different memory hierarchies. Tiramisu relies on a flexible representation based on the polyhedral model and explicitly uses a well-defined four-level IR that allows full separation between the algorithms, loop transformations, data-layouts , and communication.
After revision:  The framework is designed for the areas of image processing, stencils, linear algebra and deep learning. Tiramisu has two main features: it relies on a flexible representation based on the polyhedral model and explicitly uses a well-defined four-level IR that allows full separation between the algorithms, loop transformations, data-layouts , and communication.