API for 5G-NR Cell Allocation and Hardware Accelerator Optimization
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Solution Overview
Problem
In 5G-NR networks, existing technologies face challenges in optimizing the utilization of hardware accelerators, leading to underutilization due to unknown or unoptimized quality of service (QoS) capabilities, resulting in inefficient processing of 5G workloads.
Innovation Solution
The implementation of APIs that communicate between layer 2 and layer 1 of the O-RAN network protocol stack to determine and optimize the utilization of hardware accelerators, allowing applications to identify the maximum number of 5G-NR cells that can be supported while meeting specific QoS requirements, thereby enhancing resource allocation and processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If hardware accelerators are deployed to process 5G-NR workloads, then processing capacity increases, but resource utilization becomes inefficient due to unknown QoS capabilities
Solution Approach 1:
The patent implements a feedback mechanism where the system queries hardware accelerator QoS capabilities through API calls, receives responses about actual processing capabilities, and uses this information to optimize workload allocation. This feedback loop enables dynamic adjustment of resource allocation to match actual capabilities, resolving the contradiction between increased processing capacity and efficient resource utilization.
Solution Approach 2:
The system performs preliminary queries to hardware accelerators to determine their QoS capabilities before allocating workloads. By obtaining capability information in advance through API interactions, the system can make informed decisions about workload distribution, preventing inefficient resource utilization while maintaining high processing capacity.
2Adaptability or versatility
If more 5G-NR cells are processed concurrently, then network coverage increases, but computing resources are overwhelmed leading to performance degradation
Solution Approach 1:
The patent implements dynamic workload allocation that adjusts the number of concurrently processed cells based on real-time hardware accelerator capabilities and current system load. The system can flexibly scale the number of handled cells up or down, maintaining optimal performance while maximizing network coverage capability according to available resources.
Solution Approach 2:
The system changes operational parameters by adjusting the concurrency level of cell processing based on queried hardware capabilities and performance metrics. By dynamically modifying the number of simultaneous processing tasks, the system balances network coverage expansion with maintaining reliable processing performance.
3Device complexity
If hardware accelerator capabilities are not optimized, then implementation complexity is reduced, but resource allocation efficiency decreases
Solution Approach 1:
The patent introduces an intermediary layer (software interface/API) that sits between the workload management system and hardware accelerators. This intermediary handles the complexity of capability querying and optimization, allowing the rest of the system to remain simple while still achieving efficient resource allocation through automated capability assessment and optimization.
Data Source
AI summary
Apparatuses, systems, and techniques to perform one or more APIs. In at least one embodiment, a processor is to perform an API to indicate a number of 5G-NR cells that are able to be performed concurrently by one or more processors; a processor is to perform an API to indicate whether one or more processors are able to perform a first number of 5G-NR cells concurrently; a processor comprising one or more circuits is to perform an API to indicate whether one or more resources of one or more processors are allocated to perform 5G-NR cells; and/or a processor comprises one or more circuits to perform an API to indicate one or more techniques to be used by one or more processors in performing one or more 5G-NR cells.