AI and Neuromorphic Computing

Director: Prof. Shlomo Greenberg
 

The Research Center for Applied AI and Neuromorphic Computing was established in the Computer Science Department of SCE in 2023. The center performs theoretical and applied research in the area of machine learning and developing neuromorphic algorithms and applications.

A recently established innovative AI lab (which also serves as a teaching lab in the Department of Computer Science) provides the computing resources required for the various research projects at the center and provides unique platforms for running dedicated software packages and AI applications that require extensive computing power and support for big data.

Researchers from the academic staff, Postdocs, PhD, and MSc students perform the various types of research in the center. The research center collaborates with other Academic institutes, national labs, and local hi-tech industries.

The AI research center is directed by Prof. Shlomo Greenberg, the head of the computer science department.

The main research topics of the AI center aim to develop the following areas:

  • Develop Integrated architectures of DNN and SNN neural networks.
  • Develop AI-based algorithms and machine learning applications in diverse fields: image processing, computer vision, sound processing, signal processing, medical applications, and development of natural language models.
  • SNN-based network models and architectures for efficient and low-power implementation of real-time applications on embedded edge processors.
  • Development of neuromorphic training and learning mechanisms that attempt to mimic the learning mechanisms in biological systems.
  • Hardware and software accelerators for implementing neural networks on low-power edge devices.

Current research projects:

  • Cognitive load monitoring and Situational Awareness classification based on EEG signal analysis using a spiking neural network (SNN).
  • Early detection of epileptic seizures based on EEG signal analysis, feature extraction, and classification using SNN. (In collaboration with SensoMedical)
  • Development of hardware-based accelerator for implementing deep neural networks on low-power edge devices. (funded by the Innovation Authority)
  • Detection and identification of drones and Unmanned Aerial Vehicles using event cameras and SNN network. (within the frame of the NeMO consortium)


Prof. Shlomo Greenberg
08-6174730
shlomogr@sce.ac.il