A 1036 TOp/s/W, 12.2 mW, 2.72 μJ/Inference All Digital TNN Accelerator in 22 nm FDX Technology for TinyML Applications-Reference-Cited by-同舟云学术

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A 1036 TOp/s/W, 12.2 mW, 2.72 μJ/Inference All Digital TNN Accelerator in 22 nm FDX Technology for TinyML Applications

Published:2022-04-20 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE Symposium in Low-Power and High-Speed Chips (COOL CHIPS)
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Author:

Scherer Moritz¹,Di Mauro Alfio¹,Rutishauser Georg¹,Fischer Tim¹,Benini Luca¹

Affiliation:

1. ETH Zürich,Dept. of Information Technology and Electrical Engineering,Switzerland

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9772691/9772665/09772668.pdf?arnumber=9772668

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. David and Goliath: An Empirical Evaluation of Attacks and Defenses for QNNs at the Deep Edge;2024 IEEE 9th European Symposium on Security and Privacy (EuroS&P);2024-07-08

2. Investigation of edge computing hardware architectures processing tiny machine learning under neutron-induced radiation effects;Microelectronics Reliability;2023-11

3. REDRESS: Generating Compressed Models for Edge Inference Using Tsetlin Machines;IEEE Transactions on Pattern Analysis and Machine Intelligence;2023-09-01

4. ColibriES: A Milliwatts RISC-V Based Embedded System Leveraging Neuromorphic and Neural Networks Hardware Accelerators for Low-Latency Closed-loop Control Applications;2023 IEEE International Symposium on Circuits and Systems (ISCAS);2023-05-21

5. PetaOps/W edge-AI $\mu$ Processors: Myth or reality?;2023 Design, Automation & Test in Europe Conference & Exhibition (DATE);2023-04

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