Manuele Rusci
MSCA Post-doctoral fellow at KU Leuven, PSI division
![bio_conf.jpg](/assets/img/pers/bio_conf.jpg?d112e1dfe09592929629309ba962547b)
I am specialized in Embedded Machine Learning and Deep Learning (aka TinyML) for ultra-low power IoT sensor devices, and multi-core RISC-V Microcontrollers in particular. My contributions concern low-bitwith quantization methods and optimized software frameworks for inference on MCUs. I have also worked on full-system integration of smart audio or video sensor solutions with a power consumpions as low as few mWs (with GreenWaves Technologies).
Recently, my research focus has moved towards On-Device Learning to improve at runtime the predicion capacitiy of IoT smart sensors. In this space, I am investigating the integration of training algorithms on MCUs and efficient (continual) learning strategies for resource-constrained platforms. Thanks to my EU-funded Marie-Curie grant (on-going at KU Leuven with Prof. Tinne Tuytelaars), I am now studying novel adaptation mechanisms for ultra-low power speech recognition sensors (SEA2Learn project).
Previously, I was at Universita’ di Bologna (Prof. Benini’s group), where I was involved in the PULP project - targeting new RISC-V based IoT systems - and sevaral projects in the digital design domain, e.g., FPGA-based equipments for railway applications or, during my PhD, the design of smart camera sensors with event-based cameras.
News
Nov 30, 2023 | The new tutorial On-Device Continual Learning Meets Ultra-Low Power Processing, organized with C. Cioflan (ETHZ) and D. Nadalini (UNIBO) has been accepted at DATE24 in Valencia! |
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Sep 1, 2023 | Our paper On-device Customization of Tiny Deep Learning Models for Keyword Spotting with Few Examples, has been accepted for publication at IEEE micro! |
Aug 22, 2023 | Presenting our latest paper at Interspeech 2023 in the poster session. |
Jul 15, 2023 | New paper accepted for publication in the Special Issue of Future Generation Computer Systems journal: Reduced Precision Floating-Point Optimization for Deep Neural Network On-Device Learning on MicroControllers led by Davide Nadalini. The code is also available. |
Jun 1, 2023 | Landed in Grenoble ![]() |
Selected Publications
- On-device Customization of Tiny Deep Learning Models for Keyword Spotting with Few ExamplesIEEE Micro, 2023
- Reduced precision floating-point optimization for Deep Neural Network On-Device Learning on microcontrollersFuture Generation Computer Systems, 2023
- Accelerating RNN-Based Speech Enhancement on a Multi-core MCU with Mixed FP16-INT8 Post-training QuantizationIn Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2022
- A tinyml platform for on-device continual learning with quantized latent replaysIEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2021
- Memory-driven mixed low precision quantization for enabling deep network inference on microcontrollersProceedings of Machine Learning and Systems, 2020
- PULP-NN: accelerating quantized neural networks on parallel ultra-low-power RISC-V processorsPhilosophical Transactions of the Royal Society A, 2020