Our society is facing grand challenges in the coming decades. The growing world population necessitates more efficient use of Earth’s resources, be efficient in mobility and manufacturing. Sensing technology is in a unique position to aid in tackling all aforementioned societal challenges.
By being able to continuously sense, monitor and analyze a large amount of parameters in real time, smart objects can gain more knowledge on their environment and of their impact on this environment. Yet, current sensing technology is fundamentally held back by the amount of information it can extract from the environment given the limited amount of resources sensors have available. My goal is to develop a disruptive class of multi‐modal sensor processing platforms towards real‐time embedded sensor fusion in resource‐scarce devices.
To achieve this, we take inspiration from human sensing. We, humans, are masters in sensor fusion, as we can seamlessly combine information coming from different senses to improve our judgements. In order to effectively exploit the many sensory streams available to us, we however do not devote the same level of attention, or mental effort, to each of them. This dynamic attention-scalability allows us to always extract the maximum amount of relevant information under our limited human computational bandwidth.
Re-SENSE ambitions to bring this capability to electronic devices with limited computational resources. Specifically, dynamic attention-scalability in embedded sensing will be pursued by innovating on resource-aware sensor fusion algorithms, together with dynamic, wide-range resource-scalable sensor fusion processors. This synergistic interaction between machine learning and processor design promises at least an order of magnitude efficiency improvement in resource-scarce sensory systems for e.g. robotics or health care.
