Oct 20, 2021 · In this work, we introduce a HW/SW platform for end-to-end CL based on a 10-core FP32-enabled parallel ultra-low-power (PULP) processor.

Dec 13, 2021 · In this work, we introduce a HW/SW platform for end-to-end CL based on a 10-core FP32-enabled parallel ultra-low-power (PULP) processor. We ...

A TinyML Platform for On-Device Continual Learning With Quantized Latent Replays. Mendeley · CSV · RIS · BibTeX ; Author. Ravaglia, Leonardo · Rusci, Manuele.

May 14, 2024 · A TinyML Platform for On-Device Continual Learning with Quantized Latent Replays. By Leonardo Ravaglia, Manuele Rusci, Davide Nadalini ...

In this work, we introduce a HW/SW platform for end-to-end CL based on a 10-core FP32 -enabled parallel ultra-low-power (PULP) processor. We rethink the ...

Jul 29, 2021 · EMEA 2021 Student Forum TinyML Platform for On-Device Continual Learning with Quantized Latent Replays Leonardo RAVAGLIA, PhD Student, ...

A TinyML Platform for On-Device Continual Learning with Quantized Latent Replays ... In this work, we introduce a HW/SW platform for end-to-end CL based on a 10- ...

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 ...

A tinyml platform for on-device continual learning with quantized latent replays. L Ravaglia, M Rusci, D Nadalini, A Capotondi, F Conti, L Benini.

Alcohol Sensor Calibration on the Edge Using Tiny Machine Learning (Tiny-ML) Hardware ... A TinyML Platform for On-Device Continual Learning With Quantized Latent ...