8 citations · 17 across the 9 of their papers we have counts for
5 papers · 1 filter
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…
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…
TCNCA: Temporal Convolution Network with Chunked Attention for Scalable Sequence Processing
Aleksandar Terzic, Michael Hersche, Geethan Karunaratne +3
MEGA is a recent transformer-based architecture, which utilizes a linear recurrent operator whose parallel computation, based on the FFT, scales as , with being the s…
MIMONets: Multiple-Input-Multiple-Output Neural Networks Exploiting Computation in Superposition
Nicolas Menet, Michael Hersche, Geethan Karunaratne +3
With the advent of deep learning, progressively larger neural networks have been designed to solve complex tasks. We take advantage of these capacity-rich models to lower the cost…
In-memory Realization of In-situ Few-shot Continual Learning with a Dynamically Evolving Explicit Memory
Geethan Karunaratne, Michael Hersche, Jovin Langenegger +15
Continually learning new classes from a few training examples without forgetting previous old classes demands a flexible architecture with an inevitably growing portion of storage,…