An Integer-Only Resource-Minimized RNN on FPGA for Low-Frequency Sensors in Edge-AI
Author:
Affiliation:
1. Nano Sensing Unit, Tokyo Institute of Technology, Yokohama, Japan
2. Tokyo Institute of Technology, Research Institute for the Earth Inclusive Sensing (EISESiV), Tokyo, Japan
Funder
Japan Science and Technology Agency, Support for Pioneering Researcher Initated by the Next Generation Program
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/7361/10198840/10161725.pdf?arnumber=10161725
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1. Democratizing Domain-Specific Computing
2. A 216 μW, 87% Accurate Cow Behavior Classifying Decision Tree on FPGA with Interpolated Arctan2
3. Automated HW/SW co-design for edge AI
4. Wildlife Monitoring on the Edge: A Performance Evaluation of Embedded Neural Networks on Microcontrollers for Animal Behavior Classification
5. Recent Trends in Deep Learning Based Natural Language Processing [Review Article]
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Towards Digital Synthesis of Variable Q-Factor Direct-Conversion for Low-Power Edge Sensing;2023 IEEE SENSORS;2023-10-29
2. An Integer-Only Resource-Minimized RNN on FPGA for Low-Frequency Sensors in Edge-AI;IEEE Sensors Journal;2023-08-01
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