Postdoctoral Researcher Edge AI for sensor fusion
The objective of this postdoc topic is to research energy-efficient compute hardware together with closely coupled software methods and tools. AI will become increasingly embedded in edge hardware and matching software. This has the well-known benefits of reducing power consumption, latency and privacy concerns in real-world applications (traffic regulation, autonomous driving, drones, robotics,...). Such edge AI applications require custom hardware and novel software paradigms, which are the focus of this proposal.
You will work together with imec’s hardware teams to create edge AI algorithms that can perform sensor fusion based on appropriate low-power, low-latency algorithms, like e.g. spiking neural networks (SNN). Studying new neuron models that allow low-power compute will also be in scope. Hardware paradigms which can facilitate the creation of new AI accelerator paradigms, like probabilistic bits or analog in-memory compute will also be part of the research. Therefore, you should have an interest in both the software and the hardware domains and should be able to conceptually bridge both areas.
Application domains can be diverse, but we foresee initial applications in the automotive sector, robotics, and traffic management. In the application domains, you will focus on methods to make the output of the sensor fusion algorithms, which treat the various types of input sensor modalities, more fit-for-purpose and more compact in their representation. The challenge will be to do this in a way that is power-efficient, low-latency and respectful of privacy, so it can operate on the edge in a real-world environment. In addition, the objective will be to map the different parts of the ML processing pipeline to computational components whose architectures are best suited to the domains under study (e.g., the move towards zonal architectures in the automotive industry).
We will focus on the following research questions, which will be refined during your stay at imec, together with you:
- How can Edge AI algorithms be helpful in reducing the power budget and the latency of such data fusion algorithms?
- How can edge AI sensor fusion algorithms make optimal use of the latest generations of AI accelerator chips?
- What nascent edge AI hardware paradigms show promise to support AI sensor fusion algorithms at the edge?
You will work with teams from various parts of imec, working in a highly applied way towards contributions related to imec’s incubating edge AI program. You will also contribute to the definition of the research roadmap and will get the opportunity to support junior researchers. The focus of your research will be on addressing the above research questions through the creation and evaluation of real-world demonstrators with industrial clients in either the automotive, traffic management, robotics, or life sciences domains. You will join the imec AI group (EDiT), which focusses on research and engineering in the domain of Edge AI. The team is multidisciplinary and highly international, composed of talent with skills in ML algorithms, sensor fusion techniques, MLOps pipelines and general application development. EDiT is currently a team of around 100 people, operating from the imec offices in Ghent, Antwerp and Leuven. Its main emphasis is on software/hardware co-design and your work will be part of an ongoing effort for disruptive innovation through creative collaboration between the hardware and software departments at imec.
What we do for you
We offer you the opportunity to join one of the world’s premier research centers in nanotechnology at its headquarters in Leuven, Belgium. With your talent, passion and expertise, you’ll become part of a team that makes the impossible possible. Together, we shape the technology that will determine the society of tomorrow.
We are committed to being an inclusive employer and proud of our open, multicultural, and informal working environment with ample possibilities to take initiative and show responsibility. We commit to supporting and guiding you in this process; not only with words but also with tangible actions. Through imec.academy, 'our corporate university', we actively invest in your development to further your technical and personal growth.
We are aware that your valuable contribution makes imec a top player in its field. Your energy and commitment are therefore appreciated by means of a competitive salary with many fringe benefits.
The following papers are indicative of the intended research scope:
- [Vogginger2022] Vogginger B, Kreutz F, López-Randulfe J, Liu C, Dietrich R, Gonzalez HA, Scholz D, Reeb N, Auge D, Hille J, Arsalan M, Mirus F, Grassmann C, Knoll A and Mayr C (2022) Automotive Radar Processing With Spiking Neural Networks: Concepts and Challenges. Front. Neurosci. 16:851774. doi: 10.3389/fnins.2022.851774
- [Knobloch2022] Neuromorphic AI - An Automotive Application View of Event Based Processing, K. Knobloch, P. Gerhards, Infineon Development Center Automotive Electronics & AI 2022-06-29
- [Cordone2022] Cordone, L., Miramond, B., & Thiérion, P. (2022). Object Detection with Spiking Neural Networks on Automotive Event Data. 2022 International Joint Conference on Neural Networks (IJCNN), 1-8.
- [Kim2020] Kim, Seijoon, Seongsik Park, Byunggook Na and Sungroh Yoon. “Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection.” AAAI (2020).
- [Xiang2022] Xiang, S.; Jiang, S.; Liu, X.; Zhang, T.; Yu, L. Spiking VGG7: Deep Convolutional Spiking Neural Network with Direct Training for Object Recognition. Electronics 2022, 11,2097. https://doi.org/10.3390/ electronics11132097
- [Safa2021] Safa, Ali & Corradi, Federico & Keuninckx, Lars & Ocket, Ilja & Bourdoux, Andre & Catthoor, Francky & Gielen, Georges. (2021). Improving the Accuracy of Spiking Neural Networks for Radar Gesture Recognition Through Preprocessing. IEEE Transactions on Neural Networks and Learning Systems. PP. 1-13. 10.1109/TNNLS.2021.3109958.
- [Tsang2021] Tsang,I.J.;Corradi,F.; Sifalakis, M.; Van Leekwijck, W.; Latré, S. Radar-Based Hand Gesture Recognition Using Spiking Neural Networks. Electronics 2021, 10, 1405. https://doi.org/10.3390/electronics 1012
- [Stuijt2021] μBrain: An Event-Driven and Fully Synthesizable Architecture for Spiking Neural Networks, Front. Neurosci., 19 May 2021 https://doi.org/10.3389/fnins.2021.664208
- [Schuman2022] Schuman, C.D., Kulkarni, S.R., Parsa, M. et al. Opportunities for neuromorphic computing algorithms and applications. Nat Comput Sci 2, 10–19 (2022). https://doi.org/10.1038/s43588-021-00184-y
- [Tavanaei2019] Tavanaei, Ghodrati, Kheradpisheh, Masquelier, Maida, Deep learning in spiking neural networks, Neural Networks, Volume 111, 2019, PP 47-63, https://doi.org/10.1016/j.neunet.2018.12.002.
- [Mei2021] Mei, L., Houshmand, P., Jain, V., Giraldo, S., & Verhelst, M. (2021). Zigzag: Enlarging joint architecture-mapping design space exploration for dnn accelerators. IEEE Transactions on Computers, 70(8), 1160-1174.
- [Keuninckx2018] Keuninckx, L., Danckaert, J., & Van der Sande, G. (2018). Monostable multivibrators as novel artificial neurons. Neural Networks, 108, 224-239.