A 184-<i>μ</i>W Error-Tolerant Real-Time Hand Gesture Recognition System With Hybrid Tiny Classifiers Utilizing Edge CNN-Reference-Cited by-同舟云学术

A 184-μW Error-Tolerant Real-Time Hand Gesture Recognition System With Hybrid Tiny Classifiers Utilizing Edge CNN

Published:2023-02 Issue:2 Volume:58 Page:530-542
ISSN:0018-9200
Container-title:IEEE Journal of Solid-State Circuits
language:
Short-container-title:IEEE J. Solid-State Circuits

Author:

Lu Yuncheng¹^ORCID,Le Van Loi²^ORCID,Kim Tony Tae-Hyoung¹^ORCID

Affiliation:

1. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore

2. Nations Innovation Technologies, Singapore

Funder

RIE2020 ASTAR AME IAF-ICP

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/4/10027566/09792309.pdf?arnumber=9792309

Reference24 articles.

1. A low-power real-time hidden Markov model accelerator for gesture user interface on wearable devices;choi;Proc IEEE Asian Solid-State Circuits Conf (ASSCC),2021

2. On-device real-time hand gesture recognition;sung;arXiv 2111 00038,2021

3. A 1000 fps Vision Chip Based on a Dynamically Reconfigurable Hybrid Architecture Comprising a PE Array Processor and Self-Organizing Map Neural Network

4. A 19.4-nJ/Decision, 364-K Decisions/s, In-Memory Random Forest Multi-Class Inference Accelerator

5. A 213.7-µW Gesture Sensing System-On-Chip With Self-Adaptive Motion Detection and Noise-Tolerant Outermost-Edge-Based Feature Extraction in 65 nm

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