Binary-LoRAX: Low-Latency Runtime Adaptable XNOR Classifier for Semi-Autonomous Grasping with Prosthetic Hands-Reference-Cited by-同舟云学术

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Binary-LoRAX: Low-Latency Runtime Adaptable XNOR Classifier for Semi-Autonomous Grasping with Prosthetic Hands

Published:2021-05-30 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE International Conference on Robotics and Automation (ICRA)
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Author:

Fasfous Nael,Vemparala Manoj-Rohit,Frickenstein Alexander,Badawy Mohamed,Hundhausen Felix,Hofer Julian,Nagaraja Naveen-Shankar,Unger Christian,Vogel Hans-Jorg,Becker Jurgen,Asfour Tamim,Stechele Walter

Funder

Deutsche Forschungsgemeinschaft

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9560720/9560666/09561045.pdf?arnumber=9561045

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. Adversarial Robustness of Multi-bit Convolutional Neural Networks;Lecture Notes in Networks and Systems;2024

2. Grasping with Humanoid Hands based on In-Hand Vision and Hardware-accelerated CNNs;2023 IEEE-RAS 22nd International Conference on Humanoid Robots (Humanoids);2023-12-12

3. SiFI-AI: A Fast and Flexible RTL Fault Simulation Framework Tailored for AI Models and Accelerators;Proceedings of the Great Lakes Symposium on VLSI 2023;2023-06-05

4. A Hardware-Aware Sampling Parameter Search for Efficient Probabilistic Object Detection;Lecture Notes in Computer Science;2023

5. Semi-autonomous control of prosthetic hands based on multimodal sensing, human grasp demonstration and user intention;Robotics and Autonomous Systems;2022-08

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