Tiramisu: A Polyhedral Compiler for Dense and Sparse Deep Learning

Speaker: Riyadh Baghdadi , MIT CSAIL

Date: Monday, September 23, 2019

Time: 2:00 PM to 3:00 PM Note: all times are in the Eastern Time Zone

Public: Yes

Location: 32-D463 (Star)

Event Type: Seminar

Room Description: Refreshments at 1:45pm.

Host: Julian Shun, MIT CSAIL

Contact: Linda Lynch, 617 715 2459, lindalynch@csail.mit.edu

Relevant URL:

Speaker URL: https://people.csail.mit.edu/baghdadi/

Speaker Photo:

Reminders to: seminars@csail.mit.edu, csail-related@lists.csail.mit.edu

Reminder Subject: TALK: Tiramisu: A Polyhedral Compiler for Dense and Sparse Deep Learning

Abstract: Tiramisu is a polyhedral compiler for deep learning. It has two unique features: (1) it is the first sparse DNN compiler; and (2) it can express and optimize general RNNs (Recurrent Neural Networks). Tiramisu relies on a flexible representation based on the polyhedral model and has a rich scheduling language allowing fine-grained control of optimizations. We show that Tiramisu matches or outperforms Intel MKL-DNN by up to 5x for sparse DNNs. We also show that Tiramisu allows many new capabilities such as wavefront parallelization for RNNs.
Bio: Riyadh Baghdadi is a postdoctoral associate at CSAIL. He got his PhD from ENS Paris. He works on the area of compiler optimization and code generation for high performance architectures. Recently, he has been interested in exploring the intersection of compilers and deep learning.

Research Areas:
Computer Architecture

Impact Areas:

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Created by Linda Lynch Email at Friday, September 13, 2019 at 2:21 PM.