@inproceedings{543e04034d6d4415a0aeeb3a3b3234bb,
title = "A Fully-integrated Gesture and Gait Processing SoC for Rehabilitation with ADC-less Mixed-signal Feature Extraction and Deep Neural Network for Classification and Online Training",
abstract = "An ultra-low-power gesture and gait classification SoC is presented for rehabilitation application featuring (1) mixed-signal feature extraction and integrated low-noise amplifier eliminating expensive ADC and digital feature extraction, (2) an integrated distributed deep neural network (DNN) ASIC supporting a scalable multi-chip neural network for sensor fusion with distortion resiliency for low-cost front end modules, (3) onchip learning of DNN engine allowing in-situ training of user specific operations. A 12-channel 65nm CMOS test chip was fabricated with 1μW power per channel, less than 3ms computation latency, on-chip training for user-specific DNN model and multi-chip networking capability.",
keywords = "deep neural network, edge processing, inter-chip communication, mixed-signal feature extraction, on-chip learning",
author = "Yijie Wei and Qiankai Cao and Jie Gu and Kofi Otseidu and Levi Hargrove",
year = "2020",
month = mar,
doi = "10.1109/CICC48029.2020.9075910",
language = "English (US)",
series = "Proceedings of the Custom Integrated Circuits Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2020 IEEE Custom Integrated Circuits Conference, CICC 2020",
address = "United States",
note = "2020 IEEE Custom Integrated Circuits Conference, CICC 2020 ; Conference date: 22-03-2020 Through 25-03-2020",
}