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
| Autor: | Levi J. Hargrove, Jie Gu, Yijie Wei, Qiankai Cao, Kofi Otseidu |
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| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
Computer science business.industry 020208 electrical & electronic engineering Feature extraction Mixed-signal integrated circuit 02 engineering and technology Sensor fusion Front and back ends 03 medical and health sciences 0302 clinical medicine Application-specific integrated circuit Scalability Hardware_INTEGRATEDCIRCUITS 0202 electrical engineering electronic engineering information engineering System on a chip business 030217 neurology & neurosurgery Computer hardware |
| Zdroj: | CICC |
| DOI: | 10.1109/cicc48029.2020.9075910 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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