[Back]


Talks and Poster Presentations (with Proceedings-Entry):

O. Bringmann, W. Ecker, I. Feldner, A. Frischknecht, C. Gerum, T. Hämäläinen, M. Hanif, M. Klaiber, D. Mueller-Gritschneder, S. Prebeck, M. Shafique:
"Automated HW/SW Co-design for Edge AI: State, Challenges and Steps Ahead";
Talk: 2021 International Conference on Hardware/Software Codesign and System Synthesis, Virtual Conference (invited); 2021-10-10 - 2021-10-15; in: "Proceedings of 2020 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ ISSS)", Association for Computing Machinery, New York, NY, United States, (2021), 11 - 20.



English 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.


"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
http://dx.doi.org/10.1145/3478684.3479261


Created from the Publication Database of the Vienna University of Technology.