Machine Learning and Neuromorphic Computing for Edge and IoT

Organizers and Chairs

Khanh Dang, University of Aizu, Japan

Anh Vu Doan , Infineon, Germany

Since Edge computing and IoT play a crucial role in the computing communicating world, one of the cutting-edge research topics is embedding Machine Learning (ML) and Neuromorphic Computing (NC) for edge and embedded Internet of Things (IoT). Embedded ML and NC require lightweight computation and communication complexity as well as green power/energy with satisfactory accuracy and quality in terms of algorithm, architecture, integrated circuit, system, standard, and application levels. The embedded ML/NC research topics can cover issues of lightweight machine learning, especially for state-of-the-art deep learning. Embedded Edge/IoT can include issues of green cyber-physical communications and network systems. 

Topics of interest include, but are not limited to:

• Machine Learning for Edge and IoT
• Neuromorphic Computing for Edge and IoT
• Optimization for Edge and IoT
• Approximate computing for Edge and IoT
• Applications for Edge and IoT
• Architecture for ML/NC 
• Communication for ML/NC
• Memory technology for ML/MC
• Emerging topics of ML/MC: photonics, three-dimensional circuits, in-memory/near-memory computing