Marco Fariselli

According to our database, Marco Fariselli authored at least 8 papers between 2020 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Self-Learning for Personalized Keyword Spotting on Ultralow-Power Audio Sensors.

Manuele Rusci, Francesco Paci, Marco Fariselli, Eric Flamand, Tinne Tuytelaars

IEEE Internet Things J., April, 2025

Enabling Real-Time Streaming Temporal Convolution Network Inference on Ultra-Low-Power Microcontrollers.

Seyed Ahmad Mirsalari, Marco Fariselli, Léo Bijar, Francesco Paci, Luca Benini, Giuseppe Tagliavini

Proceedings of the IEEE Computer Society Annual Symposium on VLSI, 2025

2024

Self-Learning for Personalized Keyword Spotting on Ultra-Low-Power Audio Sensors.

Manuele Rusci, Francesco Paci, Marco Fariselli, Eric Flamand, Tinne Tuytelaars

CoRR, 2024

2022

Accelerating RNN-Based Speech Enhancement on a Multi-core MCU with Mixed FP16-INT8 Post-training Quantization.

Manuele Rusci, Marco Fariselli, Martin Croome, Francesco Paci, Eric Flamand

Proceedings of the Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2022

2021

Low-Power License Plate Detection and Recognition on a RISC-V Multi-Core MCU-Based Vision System.

Lorenzo Lamberti, Manuele Rusci, Marco Fariselli, Francesco Paci, Luca Benini

Proceedings of the IEEE International Symposium on Circuits and Systems, 2021

Integer-Only Approximated MFCC for Ultra-Low Power Audio NN Processing on Multi-Core MCUs.

Marco Fariselli, Manuele Rusci, Joel Cambonie, Eric Flamand

Proceedings of the 3rd IEEE International Conference on Artificial Intelligence Circuits and Systems, 2021

2020

CMix-NN: Mixed Low-Precision CNN Library for Memory-Constrained Edge Devices.

Alessandro Capotondi, Manuele Rusci, Marco Fariselli, Luca Benini

IEEE Trans. Circuits Syst. II Express Briefs, 2020

Leveraging Automated Mixed-Low-Precision Quantization for Tiny Edge Microcontrollers.

Manuele Rusci, Marco Fariselli, Alessandro Capotondi, Luca Benini

Proceedings of the IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning, 2020