few-shot learning (FSL)

  • No Comments

FSL-HDnn: A 40-nm Few-Shot On-Device Learning Accelerator With Integrated Feature Extraction and Hyperdimensional Computing

FSL-HDnn: A 40-nm Few-Shot On-Device Learning Accelerator With Integrated Feature Extraction and Hyperdimensional Computing 150 150
Author(s): Weihong Xu, Chang Eun Song, Haichao Yang, Leo Liu, Meng-Fan Chang, Carlos H. Diaz, Tajana Rosing, Mingu Kang

Abstract:

This article introduces FSL-HDnn, an energy-efficient accelerator that implements the end-to-end pipeline of feature extraction (FE) and on-device few-shot learning (FSL). The accelerator addresses fundamental challenges of on-device learning (ODL) for resource-constrained edge applications through two synergistic modules: a parameter-efficient feature extractor (FE) employing weight clustering and an FSL classifier …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 12
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3709217
Publisher: IEEE

View on IEEE Xplore

  • No Comments

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data 150 150
Author(s): Douwe den Blanken, Charlotte Frenkel

Abstract:

On-device learning at the edge enables low-latency, private personalization with improved long-term robustness and reduced maintenance costs. Yet, achieving scalable, low-power (LP) end-to-end on-chip learning, especially from real-world sequential data with a limited number of examples, is an open challenge. Indeed, accelerators supporting error backpropagation optimize for learning performance at …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 16
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2025.3645640
Publisher: IEEE

View on IEEE Xplore

Search this website