SELF Lab

Smart Edge Lab for Healthcare

AI acts as a driving force for economic and social development. It sits at the forefront of the technological revolution and industrial transformation.

The rise of connected Internet-of-Things (IoT) devices has led to increasing data collection, which requires greater storage, computational power, and intelligence at the edge. This partnership between IoT and edge computing has the potential to transform data computing and yield significant benefits for industry verticals. Among the crucial edge applications, biomedical devices such as medical implants (e.g., for epilepsy early detection) have enormous potential societal impact. Another exciting application of edge computing is the neuromorphic control of soft robots, which can benefit from advanced computational capabilities at the edge to achieve highly adaptable and responsive behavior, making them suitable for medical, industrial, and environmental applications.

Today’s AI solutions are extremely power-hungry and are unsuitable for energy-constrained IoT-edge devices. Deploying AI at the edge requires new computing engines with energy efficiencies 100-1,000x better than current state-of-the-art technologies.

The SELF Lab targets the design and development of smart edge computing engines. We demonstrate their superiority for personalized healthcare, such as early epilepsy detection (a neurological disease that manifests as a brain-wide phenomenon). The computing engine will be based on computation-in-memory architecture, beyond traditional Von-Neumann. It will use memristor devices well suited to brain-inspired computing, combined with new biologically inspired learning algorithms and power-aware efficient mapping methods.

Researchers are working with a tendon-driven soft robot which simulates a tentacle. Model-based control strategy can be used to control the robot.

Researchers are working with a tendon-driven soft robot which simulates a tentacle. Model-based control strategy can be used to control the robot.

Designing custom integrated circuits (ICs) is one of the steps in hardware implementation of a neuromorphic processor. The layout of the ICs is prepared prior to submitting for the fabrication process (tape-out).

Designing custom integrated circuits (ICs) is one of the steps in hardware implementation of a neuromorphic processor. The layout of the ICs is prepared prior to submitting for the fabrication process (tape-out).

An EEG device and IC design are integrated to control a robot arm. Neuromorphic deep learning methods can be utilized to process the EEG data.

An EEG device and IC design are integrated to control a robot arm. Neuromorphic deep learning methods can be utilized to process the EEG data.

Publications

The Team

Directors

Rajendra Bishnoi

Faculty of EEMCS
Lab director

Cosimo Della Santina

Faculty of ME
Lab director

PhD's

Amin Yaldagard

Faculty of EEMCS
PhD student

Chuhan Zhang

Faculty of ME
PhD student

Associated postdocs

Ali Siddiqi

Faculty of EEMCS
Associated postdos

Anteneh Gebregiorgis

Faculty of EEMCS
Associated postdos

Associated faculty

Dante Muratore

Faculty of EEMCS
Associated faculty

Luis Miranda da Cruz

Faculty of EEMCS
Associated faculty

Christos Strydis

Faculty of EEMCS
Associated faculty

Education

Courses

2024/2025 

2023/2024  

2022/2023  

2021/2022  

2020/2021  

 

Master projects

Ongoing  

  • Collecting a dataset fo event-based cameras images for a quadruped robot, Cosimo Della Santina, Jasper van Brakel, Dielof van Loon, Erik van Huffelen, Olaremi Makinwa (2022/2023) 
  • Learning to swim using spiking neural networks, Cosimo Della Santina, Wesley Lagerweij (2022/2023) 

Partners