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Cited by 20Embedded Systems Design TechniquesIoT and Edge/Fog ComputingAdvanced Neural Network Applications

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Automated HW/SW co-design for edge AI: state, challenges and steps ahead

Open access
Bringmann, Oliver, Ecker, Wolfgang, Feldner, Ingo, Frischknecht, Adrian, Gerum, Christoph, Hämäläinen, Timo, Hanif, Muhammad Abdullah, Klaiber, Michael J., Mueller-Gritschneder, Daniel, Bernardo, Paul Palomero, Prebeck, Sebastian, Shafique, Muhammad·2021·Proceedings of the 2021 International Conference on Hardware/Software Codesign and System Synthesis
DOI 10.1145/3478684.3479261
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Abstract

Gigantic rates of data production in the era of Big Data, Internet of Thing (IoT), and Smart Cyber Physical Systems (CPS) pose incessantly escalating demands for massive data processing, storage, and transmission while continuously interacting with the physical world using edge sensors and actuators. For IoT systems, there is now a strong trend to move the intelligence from the cloud to the edge or the extreme edge (known as TinyML). Yet, this shift to edge AI systems requires to design powerful machine learning systems under very strict resource constraints. This poses a difficult design task that needs to take the complete system stack from machine learning algorithm, to model optimization and compression, to software implementation, to hardware platform and ML accelerator design into account. This paper discusses the open research challenges to achieve such a holistic Design Space Exploration for a HW/SW Co-design for Edge AI Systems and discusses the current state with three currently developed flows: one design flow for systems with tightly-coupled accelerator architectures based on RISC-V, one approach using loosely-coupled, application-specific accelerators as well as one framework that integrates software and hardware optimization techniques to built efficient Deep Neural Network (DNN) systems.

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Metadata

Authors
Bringmann, O., Ecker, W., Feldner, I., Frischknecht, A., Gerum, C., Hämäläinen, T., Hanif, M. A., Klaiber, M. J., Mueller-Gritschneder, D., Bernardo, P. P., Prebeck, S., & Shafique, M.
Affiliations
  • University of Tübingen (DE)
  • Infineon Technologies (Germany) (DE)
  • Robert Bosch (Germany) (DE)
  • Tampere University (FI)
  • New York University Abu Dhabi (AE)
  • Technical University of Munich (DE)
ORCID
Venue
Proceedings of the 2021 International Conference on Hardware/Software Codesign and System Synthesis
Publisher
ACM
Published
2021-09-30
Pages
11-20
DOI
10.1145/3478684.3479261
OpenAlex
W3204828112
Cited by
20
References
3
Citations by year
2026: 4 · 2025: 5 · 2024: 3 · 2023: 6 · 2022: 2
FWCI
2.00
Type
conference paper
Language
en
Open access
Licence
Locations
International Conference on Hardware/Software Codesign and System Synthesis (publishedVersion, cc-by)
Funders
BMBF (16ME0131)
Topics
  • Physical Sciences › Computer Science › Hardware and Architecture › Embedded Systems Design Techniques
  • Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords
edge AI, hardware-software co-design, TinyML, Internet of Things, design space exploration, model compression, deep neural network, RISC-V, application-specific accelerators

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