ENT-Lab Group Webpage

Embedded and Networked Things (ENT-Lab) Group

Department of Information Engineering and Computer Science–University of Trento

We aim to explore the cross-cutting research problems and gaps concerning the hardware and software for embedded and networked systems with extreme resource and energy constraints.
These systems will significantly impact how we live and work in the near future since there will be billions of interconnected devices. This situation will create a significant demand for more bandwidth, low-power operation, sustainability, confidentiality, security, dependability, and many more.
Our current vision is to build and deploy networked embedded devices that can harvest ambient energy,run forever without batteries and support a wide range of green applications from remote sensing to wearables. We explore:

low-power computation techniques and software building blocks
low-power wireless communication protocols
hardware for low-power computing and communication.

Please contact Kasim Sinan Yildirim for thesis and collaboration opportunities.

Publications

Open Source Releases

Thesis/Course/Research Projects

We provide the state-of-the-art microcontroller-based computing platforms, field-programmable gate arrays (FPGAs), sensors, wireless modules, energy harvesting kits, and measurement tools to create testbeds for experimental evaluations and demonstrate real-world applications.

Current Research

Hardware and software for intermittent computing on batteryless IoT
- Software runtimes, tiny operating systems, programming models, hardware platforms, PCBs
Low-power and batteryless sensor networks
- Batteryless wireless protocols, backscatter, BLE, hardware support
On-device low-power and adaptive intelligence at the (batteryless) edge
- Model compression, pruning, quantization, new neural architectures, distributed intelligence
Emerging hardware for low-power on-device intelligence
- In-memory computing, emerging non-volatile memory technologies, in-/near-sensor computing

Researchers

Gaosheng Liu Post-Doc
Topic: Batteryless Networking and Intermittent Computing
Bohao Zhang PhD
Topic: Algorithm/Hardware Co-design for Energy-minimal Intelligence on Resource Constrained Embedded Systems
TBD PhD
Topic: Zero-power network pervasive intelligence
Rizah Trokic MSc Thesis
Topic: FPGAs and intermittent computing
Hossein (Maziar) Aqasizade PhD (co-advised with Mattia Antonini)
Topic: Edge Computing, IoT, and MLOps
Imola Fodor PhD (co-advised with Giovanni Iacca)
Topic: Embedded reinforcement learning
Renan Beran Kilic PhD (co-advised with Giovanni Iacca)
Topic: Adaptive tiny machine learning
Hassan Mir PhD (co-advised with Giovanni Iacca)
Topic: Federated learning

Past Members

Zehra Gulru Cam Taskiran, In-Memory Computing and Fast Inference Visiting Scholar/Postdoc 2025
Muath Abu Lebdeh, Efficient Processing of Spiking Neural Networks: A Memory-Based Approach PhD Thesis
(co-advised with Davide Brunelli) 2025
Roberto Lorenzon Memory-Efficient Streaming Inference Architecture on Low-power Microcontrollers MSc Thesis 2025
Khakim Akhunov, Enabling Full-Fledged Parallelism on Intermittently Powered Computing PhD Thesis 2024
Welid Ben Yahmed Tiny Machine Learning for Drift Compensation of Mems Pressure Sensors MSc Thesis (co-advised with Danilo Pau) 2024
Muhammad Waseem Gaussioan Mixture Model Classsification on Embedded Devices MSc Thesis 2024
Alessandro Torissi, Ultra Low Power Electronics and Communication Methods for Energy-Harvesting Batteryless IoT Devices PhD Thesis
(co-advised with Davide Brunelli) 2023
Michele Pio Fragasso Development of an Intermittent Computing Architecture for Machine Learning on Configurable Platform MSc Thesis
(co-advised with Davide Brunelli) 2023
Eren Yıldız, Systems Support for Intermittent Computing PhD Thesis 2023
Luca Caronti Adaptive Hardware Accelerated Intermittent Deep Neural Network Inference MSc Thesis (co-advised with Davide Brunelli) 2022