[Colloquium] Reminder - Maksim Levental's Candidacy Exam 10/23/23

[Devin Davis]

This is an announcement of Maksim Levental's Candidacy Exam.
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Candidate: Maksim Levental

Date:  October 23,2023

Time: 2:00pm-3:00pm

Remote Location:

Location:  JCL 298

Title: TOWARDS AN ABSTRACTION-FREE PROGRAMMING MODEL FOR DEEP
LEARNING ACCELERATORS

Abstract: The thesis of this dissertation is that it is possible to design a programming model for expert ML developers to both represent Deep Neural Networks simultaneously at multiple levels of abstraction and generate optimal, target-specific, code for novel accelerator architectures. Specifically, that by leveraging and extending an existing compiler, we can provide both an embedded domain specific language, including tensor, vector, scalar operations, concurrency, memory hierarchy, explicit data movement, and we can perform optimal code generation for novel architectures. This thesis is derived of experience over the course of several projects (see Chapter 1), each of which involved manipulating highly abstract APIs and struggling to achieve runtime performance. We validate this thesis by leveraging and extending MLIR to provide both an embedded domain specific language and optimal code generation for AMD XDNA spatial architectures. We evaluate our framework by designing, optimizing, and benchmarking several Large Language Models.

Advisors: Ian Foster

Committee Members: Ian Foster, Kyle Chard ,Raul Fernandez, & Stephen Neuendorffer


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