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Enabling hard constraints in differentiable neural network and accelerator co-exploration

Published: 23 August 2022 Publication History

Abstract

Co-exploration of an optimal neural architecture and its hardware accelerator is an approach of rising interest which addresses the computational cost problem, especially in low-profile systems. The large co-exploration space is often handled by adopting the idea of differentiable neural architecture search. However, despite the superior search efficiency of the differentiable co-exploration, it faces a critical challenge of not being able to systematically satisfy hard constraints such as frame rate. To handle the hard constraint problem of differentiable co-exploration, we propose HDX, which searches for hard-constrained solutions without compromising the global design objectives. By manipulating the gradients in the interest of the given hard constraint, high-quality solutions satisfying the constraint can be obtained.

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Cited By

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  • (2024)CPM: A Cross-layer Power Management Facility to Enable QoS-Aware AIoT Systems2024 IEEE/ACM 32nd International Symposium on Quality of Service (IWQoS)10.1109/IWQoS61813.2024.10682859(1-10)Online publication date: 19-Jun-2024
  • (2024)A Tale of Two Domains: Exploring Efficient Architecture Design for Truly Autonomous Things2024 ACM/IEEE 51st Annual International Symposium on Computer Architecture (ISCA)10.1109/ISCA59077.2024.00022(167-181)Online publication date: 29-Jun-2024
  • (2023)Hardware-aware NAS by Genetic Optimisation with a Design Space Exploration Simulator2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW59228.2023.00222(2275-2283)Online publication date: Jun-2023

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Published In

cover image ACM Conferences
DAC '22: Proceedings of the 59th ACM/IEEE Design Automation Conference
July 2022
1462 pages
ISBN:9781450391429
DOI:10.1145/3489517
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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Publication History

Published: 23 August 2022

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  • Korea government (MSIT)

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DAC '22
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DAC '22: 59th ACM/IEEE Design Automation Conference
July 10 - 14, 2022
California, San Francisco

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Overall Acceptance Rate 1,770 of 5,499 submissions, 32%

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Cited By

View all
  • (2024)CPM: A Cross-layer Power Management Facility to Enable QoS-Aware AIoT Systems2024 IEEE/ACM 32nd International Symposium on Quality of Service (IWQoS)10.1109/IWQoS61813.2024.10682859(1-10)Online publication date: 19-Jun-2024
  • (2024)A Tale of Two Domains: Exploring Efficient Architecture Design for Truly Autonomous Things2024 ACM/IEEE 51st Annual International Symposium on Computer Architecture (ISCA)10.1109/ISCA59077.2024.00022(167-181)Online publication date: 29-Jun-2024
  • (2023)Hardware-aware NAS by Genetic Optimisation with a Design Space Exploration Simulator2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW59228.2023.00222(2275-2283)Online publication date: Jun-2023

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