Machine Learning in Resource-Constrained Embedded Systems

Machine learning (ML) methods, in the presence of deep learning techniques and big data gathered by the emergence of Internet of Things (IoT) and Cyber-Physical Systems (CPS), play a critical role in extracting meaningful information from the surrounding world. Transferring such amount of data to data-centers and clouds for storage and computational goals, either for training or for inference, may not be always possible because of costs like time, energy, network, and so on. These costs may not be acceptable for many real-world applications, including time-sensitive, battery-operated, and connectivity-limited devices. A resulting trend is thus, in-sensor/near-sensor computations and/or domain-specific architectures which perform optimizations using specialized ML accelerators. On the other hand, ML applications often need to achieve high utility/accuracy under certain resource constraints. The constraints may be changed to optimization goals as well, depending on the application type. Addressing this tradeoff is an inherent challenge that needs to be investigated in a principled fashion in order to understand the physical world more practically and effectively, raising the need to upgrade and adapt ML algorithms. In this special issue, we welcome original submissions in all theoretical and practical, yet application-specific, design and analysis methods of machine learning, deep learning, and artificial intelligence in the context of embedded systems and edge-computing.

Topics of interest include (but not limited to):

• Design methodologies for resource-constrained ML hardware accelerators
• ML workload acceleration on conventional technologies like FPGA, ASIP and ASIC
• Acceleration of domain-specific ML algorithms for edge-computing, IoT, and CPS
• Approximate ML for resource-constrained systems
• Resource-aware approximate ML
• Design methodologies for scalable ML
• Design methodologies for adaptive ML
• Metrics and methods to evaluate ML accuracy/utility
• New paradigms of ML design and implementation

Submission Guidelines:

All authors who presented quality papers at the Real-time and Embedded Systems and Technologies Conference (RTEST 2020) are highly encouraged to submit an extended version of their paper for possible inclusion in this special issue. Besides submissions based on RTEST 2020 high-quality papers, other high-quality submissions within the scope of the special issue are highly welcomed. Each submission will be reviewed by at least three reviewers to ensure a high quality of selected papers for the special issue.

The papers must be written in English and describe original research neither published nor currently under review in any other journal or conference. The call and author guidelines for preparation of manuscripts can be found at http://www.elsevier.com/locate/micpro. All manuscripts and any supplementary material should be submitted to the Elsevier online submission system Evise of the MICPRO journal, available at: https://www.evise.com/profile/#/MICPRO/login. Please select “SI: ML-RCES” from the special issue drop-down list (NOT any other special or regular issue). Please send all enquiries regarding this special issue to Hamid Sarbazi-Azad at azad@sharif.edu.

Important Dates:

Submission deadline: 25^th July 2020

Interim decision: 25^th September 2020

Revised papers submission: 25^th October 2020

Final decision: 25^th December 2020

Guest Editors:

Farshad Khunjush, Shiraz University, khunjush@cse.shirazu.ac.ir

Mehdi Kargahi, University of Tehran, kargahi@ut.ac.ir

Masoud Daneshtalab, Mälardalen University, masoud.daneshtalab@mdh.se

Hamid Sarbazi-Azad, Sharif University of Technology, and Institute for Research in Fundamental sciences (IPM), azad@sharif.edu