Huawei technologies co., ltd. (20240320511). INTERFACING WITH CODED INFERENCE NETWORKS simplified abstract
INTERFACING WITH CODED INFERENCE NETWORKS
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INTERFACING WITH CODED INFERENCE NETWORKS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240320511 titled 'INTERFACING WITH CODED INFERENCE NETWORKS
- Simplified Explanation:**
The patent application discusses the use of coding theory in inferencing with deep neural networks to improve agility, robustness, and accuracy in 6G wireless networks.
- Key Features and Innovation:**
- Utilizing coding theory in inferencing with deep neural networks - Distributed inferencing with redundant wireless bandwidths and edge units - Enhancing agility, robustness, and accuracy in coded inferencing networks
- Potential Applications:**
- AI applications - 6G wireless networks - Edge computing
- Problems Solved:**
- Ensuring agility, robustness, and accuracy in inferencing - Improving efficiency in deep neural network inferencing - Enhancing performance in wireless networks
- Benefits:**
- Improved inferencing performance - Enhanced reliability in AI applications - Increased efficiency in wireless networks
- Commercial Applications:**
- Optimization of AI applications - Development of advanced wireless network technologies - Integration of coding theory in inferencing systems
- Questions about Coding Theory in Inferencing:**
1. How does coding theory improve the agility of inferencing networks? 2. What role do edge units play in enhancing the robustness of coded inferencing networks?
- Frequently Updated Research:**
Stay updated on the latest advancements in coding theory for inferencing to ensure optimal performance in AI applications and wireless networks.
Original Abstract Submitted
some embodiments of the present disclosure relate to inferencing using a trained deep neural network. inferencing may, reasonably, be expected to be a mainstream application of 6g wireless networks. agile, robust and accurate inferencing is important for the success of ai applications. aspects of the present application relate to introducing coding theory into inferencing in a distributed manner. it may be shown that redundant wireless bandwidths and edge units help to ensure agility, robustness and accuracy in coded inferencing networks.