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Compiling Deep Learning Kernels to L...

Zhao, Tian.

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Compiling Deep Learning Kernels to Locality-Aware Dataflow.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Compiling Deep Learning Kernels to Locality-Aware Dataflow./
Author:	Zhao, Tian.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	110 p.
Notes:	Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
Contained By:	Dissertations Abstracts International84-12B.
Subject:	Programming languages. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30462707
ISBN:	9798379651602

Compiling Deep Learning Kernels to Locality-Aware Dataflow.
Zhao, Tian.

Compiling Deep Learning Kernels to Locality-Aware Dataflow. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 110 p.

Source: Dissertations Abstracts International, Volume: 84-12, Section: B.

Thesis (Ph.D.)--Stanford University, 2023.

Emerging deep learning applications require unprecedented computation and memory capacity. To accelerate these applications, novel processing systems such as dataflow accelerators strive to exploit multiple dimensions of parallelism within deep learning models, e.g., tensor and pipeline parallelism. Although these systems provide ultrahigh performance when fully utilized, compiling deep learning applications to harness their computation capability remains a challenging problem. With recent advances in domain-specific programming language, accelerator design, and machine learning, we now have the potential to better serve the needs of training and evaluating large deep learning applications on dataflow accelerators through algorithm, software, and hardware co-design.In this dissertation, I present the design and development of efficient deep learning optimizations and programming frameworks. I present two frameworks: SpatialRNN for accelerating recurrent neural network language models on spatial accelerators and Sigma for expressing and accelerating high-data-reuse deep learning kernels using reconfigurable dataflow accelerators. Our end-to-end evaluation using Sigma demonstrates a 5.4x speedup on kernels encompassing financial applications, traditional machine learning, language modeling and computer vision tasks over an Nvidia V100 GPU accelerator.

ISBN: 9798379651602Subjects--Topical Terms:

3683658
Programming languages.

Compiling Deep Learning Kernels to Locality-Aware Dataflow.
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