CMOS and beyond CMOS
Discover why imec is the premier R&D center for advanced logic & memory devices. anced logic & memory devices.
Connected health solutions
Explore the technologies that will power tomorrow’s wearable, implantable, ingestible and non-contact devices.
Life sciences
See how imec brings the power of chip technology to the world of healthcare.
Sensor solutions for IoT
Dive into innovative solutions for sensor networks, high speed networks and sensor technologies.
Artificial intelligence
Explore the possibilities and technologies of AI.
More expertises
Discover all our expertises.
Research
Be the first to reap the benefits of imec’s research by joining one of our programs or starting an exclusive bilateral collaboration.
Development
Build on our expertise for the design, prototyping and low-volume manufacturing of your innovative nanotech components and products.
Solutions
Use one of imec’s mature technologies for groundbreaking applications across a multitude of industries such as healthcare, agriculture and Industry 4.0.
Venturing and startups
Kick-start your business. Launch or expand your tech company by drawing on the funds and knowhow of imec’s ecosystem of tailored venturing support.
/Job opportunities/Mixed-signal spiking neural network cores for eftreme edge sensor processing

Mixed-signal spiking neural network cores for eftreme edge sensor processing

PhD - Leuven | More than two weeks ago

From sensors to senses - neuromorphic technologies for tomorrow's AIoT

Autonomous vehciles, smart cities, indoor smart farming, connected wearables, etc. all have one thing in common: the problem of building accurate models is currently "solved" by adding more and more sensors, creating more and more data and eventually a massive data deluge in the cloud (or edge). A way out of this impasse is to design truly intelligent sensors that not only compress their raw data towards the cloud, but also learn which features are essential to communicate towards a central hub. Using spiking neural networks with onlne learning capabilities this can be achieved, making the downstream model building feasible without relying on high bandwidth datalinks. At imec, hardware and algorithm development for SNN's has been going hand in hand for different types of sensors, ranging from wearable/implantable ECG patches to radar sensors. In this PhD, we will build further on the results for purely digital implementations and explore mixed signal designs. You will be guided by experts in signal processing, SNN's and mixed signal low-power design, with the end oal of creating a sensor fusion IC for deeply fusing event-based radar and camera streams.


Required background: Electrical engineering, neuromorphic engineering, digital design

Type of work: 60% modeling/simulation, 30% experimental, 10% literature

Supervisor: Piet Wambacq

Co-supervisor: Jan Craninckx

Daily advisor: Ilja Ocket, Lars Keuninckx

The reference code for this position is 2021-120. Mention this reference code on your application form.

This website uses cookies for analytics purposes only without any commercial intent. Find out more here. Our privacy statement can be found here. Some content (videos, iframes, forms,...) on this website will only appear when you have accepted the cookies.