on-chip training

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A 7.5-μW 35-Keyword End-to-End Keyword Spotting System With Random Augmented On-Chip Training

A 7.5-μW 35-Keyword End-to-End Keyword Spotting System With Random Augmented On-Chip Training 150 150
Author(s): Hyuk-Jin Lee, Kyunghoon Pyo, Taekwang Jang, Mingoo Seok, SeongHwan Cho

Abstract:

Fully integrated keyword spotting (KWS) systems designed for low-power operation face two major challenges. First, increasing the number of supported keywords significantly raises system complexity and power consumption. Second, most existing systems are not personalized to individual users, as they are trained on data from native English speakers, leading to …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 10
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3660634
Publisher: IEEE

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Space-Mate: A 303.5-mW Real-Time Sparse Mixture-of-Experts-Based NeRF-SLAM Processor for Mobile Spatial Computing

Space-Mate: A 303.5-mW Real-Time Sparse Mixture-of-Experts-Based NeRF-SLAM Processor for Mobile Spatial Computing 150 150
Author(s): Gwangtae Park, Seokchan Song, Haoyang Sang, Dongseok Im, Donghyeon Han, Sangyeob Kim, Hongseok Lee, Hoi-Jun Yoo

Abstract:

Simultaneous localization and mapping (SLAM) provides crucial ego-pose information and 3-D maps of the user environment, which are fundamental to emerging mobile spatial computing devices. Dense 3-D mapping and accurate pose estimation are particularly necessary for applications like augmented reality (AR) and autonomous navigation. However, existing SLAM processors are typically …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 13
Year of Publication: 2025
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2025.3610931
Publisher: IEEE

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