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ISA mapper: a compute and hardware agnostic deep learning compiler

Authors:

Matthew Sotoudeh,

Michael Anderson,

Evangelos Georganas,

Alexander Heinecke,

Jason KnightAuthors Info & Claims

CF '19: Proceedings of the 16th ACM International Conference on Computing Frontiers

Pages 164 - 173

https://doi.org/10.1145/3310273.3321559

Published: 30 April 2019 Publication History

Abstract

Domain specific accelerators present new challenges for code generation onto novel instruction sets, communication fabrics, and memory architectures. We introduce a shared intermediate representation to describe both deep learning programs and hardware capabilities, then formulate and apply instruction mapping to determine how a computation can be performed on a hardware system. Our scheduler chooses a specific mapping and determines data movement and computation order.

With this system, we demonstrate automated extraction of matrix multiplication kernels from recent deep learning operations. We demonstrate 2--5X better performance on GEMM and GRU execution versus state-of-the-art on new hardware and up to 85% of state-of-the-art performance on existing hardware.

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Cited By

Zheng SChen RWei AJin YHan QLu LWu BLi XYan SLiang YSalapura VZahran MChong FTang L(2022)AMOSProceedings of the 49th Annual International Symposium on Computer Architecture10.1145/3470496.3527440(874-887)Online publication date: 18-Jun-2022
https://dl.acm.org/doi/10.1145/3470496.3527440
Man XZhu JSong GYin SWei SLiu LSalapura VZahran MChong FTang L(2022)CaSMapProceedings of the 49th Annual International Symposium on Computer Architecture10.1145/3470496.3527426(259-273)Online publication date: 18-Jun-2022
https://dl.acm.org/doi/10.1145/3470496.3527426
Rieber DAcosta AFröning H(2021)Joint Program and Layout Transformations to Enable Convolutional Operators on Specialized Hardware Based on Constraint ProgrammingACM Transactions on Architecture and Code Optimization10.1145/348792219:1(1-26)Online publication date: 6-Dec-2021
https://dl.acm.org/doi/10.1145/3487922
Show More Cited By

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Published In

cover image ACM Conferences

CF '19: Proceedings of the 16th ACM International Conference on Computing Frontiers

April 2019

414 pages

ISBN:9781450366854

DOI:10.1145/3310273

General Chairs:
Francesca Palumbo
Universit`a degli Studi di Sassari, IT
,
Michela Becchi
North Carolina State University
,
Program Chairs:
Martin Schulz
Technical University of Munich, DE
,
Kento Sato
RIKEN R-CCS, JP

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMICRO: ACM Special Interest Group on Microarchitectural Research and Processing

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 April 2019

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CF '19

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SIGMICRO

CF '19: Computing Frontiers Conference

April 30 - May 2, 2019

Alghero, Italy

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Overall Acceptance Rate 273 of 785 submissions, 35%

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Cited By

Zheng SChen RWei AJin YHan QLu LWu BLi XYan SLiang YSalapura VZahran MChong FTang L(2022)AMOSProceedings of the 49th Annual International Symposium on Computer Architecture10.1145/3470496.3527440(874-887)Online publication date: 18-Jun-2022
https://dl.acm.org/doi/10.1145/3470496.3527440
Man XZhu JSong GYin SWei SLiu LSalapura VZahran MChong FTang L(2022)CaSMapProceedings of the 49th Annual International Symposium on Computer Architecture10.1145/3470496.3527426(259-273)Online publication date: 18-Jun-2022
https://dl.acm.org/doi/10.1145/3470496.3527426
Rieber DAcosta AFröning H(2021)Joint Program and Layout Transformations to Enable Convolutional Operators on Specialized Hardware Based on Constraint ProgrammingACM Transactions on Architecture and Code Optimization10.1145/348792219:1(1-26)Online publication date: 6-Dec-2021
https://dl.acm.org/doi/10.1145/3487922
Rieber DFröning H(2021)Search Space Complexity of Iteration Domain Based Instruction Embedding for Deep Learning AcceleratorsIoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning10.1007/978-3-030-66770-2_16(213-228)Online publication date: 10-Jan-2021
https://doi.org/10.1007/978-3-030-66770-2_16
Sotoudeh MThakur AKell SVerna D(2020)Analogy-making as a Core primitive in the software engineering toolboxProceedings of the 2020 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software10.1145/3426428.3426918(101-121)Online publication date: 18-Nov-2020
https://dl.acm.org/doi/10.1145/3426428.3426918
Kim RKim GKim HYoon GYoo H(2020)A Method for Optimizing Deep Learning Object Detection in Edge Computing2020 International Conference on Information and Communication Technology Convergence (ICTC)10.1109/ICTC49870.2020.9289529(1164-1167)Online publication date: 21-Oct-2020
https://doi.org/10.1109/ICTC49870.2020.9289529

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