Special Session: Approximate TinyML Systems: Full System Approximations for Extreme Energy-Efficiency in Intelligent Edge Devices-Reference-Cited by-同舟云学术

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Special Session: Approximate TinyML Systems: Full System Approximations for Extreme Energy-Efficiency in Intelligent Edge Devices

Published:2021-10 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE 39th International Conference on Computer Design (ICCD)
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Author:

Raha Arnab,Ghosh Soumendu,Mohapatra Debabrata,Mathaikutty Deepak A.,Sung Raymond,Brick Cormac,Raghunathan Vijay

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9643591/9643617/09643771.pdf?arnumber=9643771

Cited by 6 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. Toward Energy-Efficient Collaborative Inference Using Multisystem Approximations;IEEE Internet of Things Journal;2024-05-15

2. PArtNNer: Platform-Agnostic Adaptive Edge-Cloud DNN Partitioning for Minimizing End-to-End Latency;ACM Transactions on Embedded Computing Systems;2024-01-10

3. Comparison of edge computing methods in Internet of Things architectures for efficient estimation of indoor environmental parameters with Machine Learning;Engineering Applications of Artificial Intelligence;2023-11

4. Efficient Hardware Acceleration of Emerging Neural Networks for Embedded Machine Learning: An Industry Perspective;Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing;2023-10-01

5. Energy-Efficient Approximate Edge Inference Systems;ACM Transactions on Embedded Computing Systems;2023-07-24

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