Towards Full Forward On-Tiny-Device Learning: A Guided Search for a Randomly Initialized Neural Network

Towards Full Forward On-Tiny-Device Learning: A Guided Search for a Randomly Initialized Neural Network

In the context of TinyML, many research efforts have been devoted to designing forward topologies to support On-Device Learning. Reaching this target would bring numerous advantages, including reductions in latency and computational complexity, stronger privacy, data safety and robustness to adversa...

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Bibliographic Details
Main Authors: Danilo Pau, Andrea Pisani, Antonio Candelieri
Format: Article
Language:English
Published: MDPI AG 2024-01-01
Series:Algorithms
Subjects:
Bayesian optimization
extreme learning machine
feature extraction
hyperparameter optimization
neural architecture search
on-tiny-device learning
Online Access:https://www.mdpi.com/1999-4893/17/1/22

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