The Internet of Things (IoT) is enabling and multiplying the point of collection of multimodal data such as audio, video and environmental data, typically processed in the cloud where potentially infinite computational power is available. However, this comes at the cost of bandwidth, energy and privacy. Recent research in the so-called tinyML domain is tackling the challenge of bringing artificial intelligence to the end-devices, thus limiting the need to stream data to the cloud and implementing the distributed intelligence paradigm in the cloud-edge continuum. Thus, novel approaches to enable AI, typically computationally demanding, on resource-limited devices are needed.  Exciting research scenarios are emerging to enable inference at the edge, ranging from distillation, quantization, or neural architecture search strategies to the fusion of software techniques with innovative hardware supporting tinyML. The complexity grows when we consider shifting not only inference but also learning to the edge to harness the opportunities offered by connected, distributed devices. The research proposed will focus on one or more of the following goals: (i) Develop novel hardware and software approaches to optimize AI on energy-efficient embedded devices, with a particular emphasis on audio processing and computer vision, while also considering other domains. (ii) Explore the potential of distributing and integrating intelligence from heterogeneous nodes in an IoT environment. (iii) Demonstrate the benefits of these approaches in real-world application scenarios, such as those encountered in smart cities.

This interdisciplinary research at the intersection of Artificial Intelligence, Embedded Systems, Distributed Computing, and Low-power Hardware will take into account the candidate’s profile and interests, contributing to the development of innovative solutions for real-world challenges. The candidate will have the opportunity to work with cutting-edge technology, gain valuable experience in interdisciplinary collaboration, and make significant contributions to the field of tiny machine learning.