Qualcomm had three patents in edge computing during Q2 2024. Qualcomm Inc’s patents filed in Q2 2024 focus on intelligent cloud-edge resource management and optimizing the compilation of machine learning models for target edge devices. The first patent discusses how end devices can provide network link information to edge nodes in order to improve task workload scheduling and distribution, leading to better performance, power efficiency, security, and network availability. The second patent outlines a method for allocating compute nodes to optimize the compilation process of a machine learning model, ultimately selecting the best solution for execution on target edge devices based on performance metrics. GlobalData’s report on Qualcomm gives a 360-degree view of the company including its patenting strategy. Buy the report here.
Qualcomm had no grants in edge computing as a theme in Q2 2024.
Recent Patents
Application: Intelligent cloud-edge resource management (Patent ID: US20240195867A1)
The patent filed by Qualcomm Inc. discloses systems, methods, and apparatuses for intelligent cloud-edge resource management. The end device provides network link information to edge nodes in an edge computing system, enabling more effective scheduling and distribution of task workloads for improved performance, power efficiency, security, and network availability. The end device can assign processing tasks to edge nodes based on parameters and estimated completion times, optimizing task distribution. The apparatus includes interfaces for obtaining network link information, processing task requests, and task parameters, as well as for outputting task assignments to edge nodes based on various factors such as latency thresholds, task priorities, and compute resource availability.
Furthermore, the apparatus can maintain coherency across edge nodes, reassign tasks from offline nodes, and manage resource allocation based on predictive resource availability and wireless communication link quality. The network link information includes details on communication link topology, edge node processing capabilities, quality metrics, battery levels, power status, workload information, and predictive topology changes. The apparatus also supports different workload types, task prioritization policies, and security requirements, enhancing the efficiency and effectiveness of edge compute resource orchestration. Overall, the disclosed systems and methods aim to optimize task distribution in edge computing environments for enhanced performance and resource utilization.
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