Context
AI is proliferating everywhere even to embedded systems to integrate intelligence closer to the sensors (IoT, drones, vehicles, satellites …). But the energy consumption of current Deep learning solutions makes classical AI hardly compatible with energy and resource constrained devices. Edge AI is a recent subject of research that needs to take into account the cost of the neural models both during the training and during the prediction. An original and promising solution to face these constraints is to merge compression technics of deep neural networks and event-based encoding of information thanks to Spiking neural networks (SNN). SNN are considered as third generation of artificial neural networks and are inspired from the way the information is encoded in the brain, and previous works tend to
conclude that SNN are more efficient than classical deep networks [3]. This internship project aims at confirming this assumption by converting classical CNN to SNN from standard Machine Learning frameworks (Keras) and deploy the resulting neural models onto the Akida neuromorphic processor from BrainChip company [4]. The results obtained in terms of accuracy, latency and energy will be compared to other existing embedded solutions for Edge AI [2].
Project mission
The project mission will be organized in several periods:
- Bibliographic study on spiking neural network training
- Introduction to the existing Sw framework from BrainChip
- Training of convolutional neural networks for embedded applications [1] and conversion from CNN to SNN from Keras
- Deployment of the SNN onto Akida processing platform
- Experimentations and measurements
- Publication in an international conference.
References
[1] L Cordone, B Miramond, P Thierion, Object Detection with Spiking Neural Networks on Automotive Event
Data, IEEE International Joint Conference on Neural Networks (IJCNN), 2022
[2] N Abderrahmane, B Miramond, E Kervennic, A Girard, SPLEAT: SPiking Low-power Event-based ArchiTecture
for in-orbit processing of satellite imagery, IEEE International Joint Conference on Neural Networks, 1-10, 2022
[3] E Lemaire, L Cordone, A Castagnetti, PE Novac, J Courtois, B Miramond, An Analytical Estimation of Spiking
Neural Networks Energy Efficiency, Springer International Conference on Neural Information Processing, 2022
[4] T. Álvarez-Sánchez, et al, Detection of facial emotions using neuromorphic computation, Applications of
Digital Image Processing, 2022
Practical information
Location : LEAT Lab / SophiaTech Campus, Sophia Antipolis
Duration : 6 months from march 2023
Grant : from ANR project DeepSee
Profile : Machine learning, Artificial intelligence, Artificial neural networks, Python, Keras, Pytorch
Research keywords : Spiking neural network, Edge AI, neuromorphic computing
Contact and supervision
Benoît Miramond, Andrea Castagnetti
LEAT Lab – University Cote d’Azur / CNRS
Polytech Nice Sophia
04.89.15.44.39. / benoit.miramond@univ-cotedazur.fr