8 citations · 17 across the 9 of their papers we have counts for
4 papers · 1 filter
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…
A Precision-Optimized Fixed-Point Near-Memory Digital Processing Unit for Analog In-Memory Computing
Elena Ferro, Athanasios Vasilopoulos, Corey Lammie +4
Analog In-Memory Computing (AIMC) is an emerging technology for fast and energy-efficient Deep Learning (DL) inference. However, a certain amount of digital post-processing is requ…
Zero-shot Classification using Hyperdimensional Computing
Samuele Ruffino, Geethan Karunaratne, Michael Hersche +3
Classification based on Zero-shot Learning (ZSL) is the ability of a model to classify inputs into novel classes on which the model has not previously seen any training examples. P…