5 citations · 6 across the 7 of their papers we have counts for
8 papers
NanoHydra: Energy-Efficient Time-Series Classification at the Edge
Cristian Cioflan, Jose Fonseca, Xiaying Wang +1
Time series classification (TSC) on extreme edge devices represents a stepping stone towards intelligent sensor nodes that preserve user privacy and offer real-time predictions. Re…
On-Device Federated Continual Learning on RISC-V-based Ultra-Low-Power SoC for Intelligent Nano-Drone Swarms
Lars Kröger, Cristian Cioflan, Victor Kartsch +1
RISC-V-based architectures are paving the way for efficient On-Device Learning (ODL) in smart edge devices. When applied across multiple nodes, ODL enables the creation of intellig…
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…
MS-RANAS: Multi-Scale Resource-Aware Neural Architecture Search
Cristian Cioflan, Radu Timofte
Neural Architecture Search (NAS) has proved effective in offering outperforming alternatives to handcrafted neural networks. In this paper we analyse the benefits of NAS for image…