Name
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/CICC48029.2020.9075910 | 2020 IEEE Custom Integrated Circuits Conference (CICC) |
Keywords | DocType | ISSN |
edge processing,deep neural network,inter-chip communication,mixed-signal feature extraction,on-chip learning | Conference | 0886-5930 |
ISBN | Citations | PageRank |
978-1-7281-6032-0 | 0 | 0.34 |
References | Authors | |
1 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yijie Wei | 1 | 0 | 1.69 |
| Qiankai Cao | 2 | 0 | 0.34 |
| Jie Gu | 3 | 9 | 5.59 |
| Kofi Otseidu | 4 | 0 | 1.35 |
| Levi J Hargrove | 5 | 438 | 42.47 |