Accurate Estimation of the CNN Inference Cost for TinyML Devices
Author:
Affiliation:
1. Sorbonne Université,CNRS, LIP6,Paris,France,F-75005
2. CY Cergy Paris University, ENSEA, CNRS,ETIS, UMR 8051,Cergy,France,F-95000
3. Wisebatt,Paris,France
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9907764/9908069/09908108.pdf?arnumber=9908108
Reference22 articles.
1. CNN Inference Costs Estimation on Microcontrollers: the EST Primitive-based Model
2. µnas: Constrained neural architecture search for microcontrollers;liberis;CoRR,2020
3. Phinets: a scalable backbone for low-power AI at the edge;paissan;CoRR,2021
4. NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications
5. Measuring what really matters: Optimizing neural networks for tinyml;heim;CoRR,2021
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1. Work-In-Progress: Could Tensorflow Applications Benefit from a Mixed-Criticality Approach?;2023 IEEE Real-Time Systems Symposium (RTSS);2023-12-05
2. An Empirical Study of Convolutional Neural Network Compressions within Low-Power Devices;2023 30th IEEE International Conference on Electronics, Circuits and Systems (ICECS);2023-12-04
3. TinyMM: Multimodal-Multitask Machine Learning on Low-Power MCUs for Smart Glasses;2023 IEEE SENSORS;2023-10-29
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