자료유형  

 학위논문

Control Number  

0016931988

International Standard Book Number  

9798379651602

Dewey Decimal Classification Number  

005

Main Entry-Personal Name  

Zhao, Tian.

Publication, Distribution, etc. (Imprint  

[S.l.] : Stanford University., 2023

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2023

Physical Description  

1 online resource(110 p.)

General Note  

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

General Note  

Advisor: Raina, Priyanka;Re, Christopher;Olukotun, Oyekunle.

Dissertation Note  

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

Restrictions on Access Note  

This item must not be sold to any third party vendors.

Summary, Etc.  

요약Emerging 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.

Subject Added Entry-Topical Term  

Programming languages.

Subject Added Entry-Topical Term  

Deep learning.

Subject Added Entry-Topical Term  

Bandwidths.

Subject Added Entry-Topical Term  

Optimization techniques.

Subject Added Entry-Topical Term  

Neural networks.

Subject Added Entry-Topical Term  

Design.

Subject Added Entry-Topical Term  

Keyboards.

Subject Added Entry-Topical Term  

Linear algebra.

Subject Added Entry-Topical Term  

Computer science.

Subject Added Entry-Topical Term  

Mathematics.

Added Entry-Corporate Name  

Stanford University.

Host Item Entry  

Dissertations Abstracts International. 84-12B.

Host Item Entry  

Dissertation Abstract International

Electronic Location and Access  

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Control Number  

joongbu:643217