5 citations · 6 across the 5 of their papers we have counts for
5 papers
Train-On-Request: An On-Device Continual Learning Workflow for Adaptive Real-World Brain Machine Interfaces
Lan Mei, Cristian Cioflan, Thorir Mar Ingolfsson +4
Brain-machine interfaces (BMIs) are expanding beyond clinical settings thanks to advances in hardware and algorithms. However, they still face challenges in user-friendliness and s…
An Ultra-Low Power Wearable BMI System with Continual Learning Capabilities
Lan Mei, Thorir Mar Ingolfsson, Cristian Cioflan +4
Driven by the progress in efficient embedded processing, there is an accelerating trend toward running machine learning models directly on wearable Brain-Machine Interfaces (BMIs)…
On-Device Domain Learning for Keyword Spotting on Low-Power Extreme Edge Embedded Systems
Cristian Cioflan, Lukas Cavigelli, Manuele Rusci +2
Keyword spotting accuracy degrades when neural networks are exposed to noisy environments. On-site adaptation to previously unseen noise is crucial to recovering accuracy loss, and…
12 mJ per Class On-Device Online Few-Shot Class-Incremental Learning
Yoga Esa Wibowo, Cristian Cioflan, Thorir Mar Ingolfsson +4
Few-Shot Class-Incremental Learning (FSCIL) enables machine learning systems to expand their inference capabilities to new classes using only a few labeled examples, without forget…
Boosting keyword spotting through on-device learnable user speech characteristics
Cristian Cioflan, Lukas Cavigelli, Luca Benini
Keyword spotting systems for always-on TinyML-constrained applications require on-site tuning to boost the accuracy of offline trained classifiers when deployed in unseen inference…