Hardware Acceleration for Postdecision State Reinforcement Learning in IoT Systems-Reference-Cited by-同舟云学术

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Hardware Acceleration for Postdecision State Reinforcement Learning in IoT Systems

Published:2022-06-15 Issue:12 Volume:9 Page:9889-9903
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Sun Jianchi¹^ORCID,Sharma Nikhilesh²^ORCID,Chakareski Jacob³^ORCID,Mastronarde Nicholas²^ORCID,Lao Yingjie¹^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, Clemson University, Clemson, SC, USA

2. Department of Electrical Engineering, University at Buffalo, Buffalo, NY, USA

3. Department of Informatics, New Jersey Institute of Technology, Newark, NJ, USA

Funder

National Science Foundation

NSF

Panasonic Chair of Sustainability at NJIT

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Link

https://ieeexplore.ieee.org/ielam/6488907/9789736/9745086-aam.pdf

Reference68 articles.

1. Post-Quantum Cryptoprocessors Optimized for Edge and Resource-Constrained Devices in IoT

2. Energy Efficient and Side-Channel Secure Cryptographic Hardware for IoT-Edge Nodes

3. Fast and low-complexity reinforcement learning for delay-sensitive energy harvesting wireless visual sensing systems

4. Fast Reinforcement Learning for Energy-Efficient Wireless Communication

5. Deep Learning Models for Wireless Signal Classification With Distributed Low-Cost Spectrum Sensors

Cited by 3 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. M-ary Distributed Decision Fusion for Multihop Relay Wireless Sensor Networks: Decision Fusion Rule, Implementation Framework, and Performance Analysis;IEEE Internet of Things Journal;2024-09-15

2. Blind Post-Decision State-Based Reinforcement Learning for Intelligent IoT;IEEE Internet of Things Journal;2023-06-15

3. Automatic IP Core Generator for FPGA-Based Q-Learning Hardware Accelerators;Lecture Notes in Electrical Engineering;2023

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