Converged Baseband and AI Hardware for Shared Resource Allocation
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Solution Overview
Problem
Existing wireless communication systems face inefficiencies due to dedicated hardware acceleration for specific wireless standards and AI operations, leading to resource waste and lack of operational flexibility.
Innovation Solution
Implementing a unified hardware acceleration system that integrates software-configurable converged modems for simultaneous processing of different wireless standards and AI operations, sharing computational resources and optimizing hardware usage through dynamic resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If dedicated hardware acceleration is implemented for specific wireless standards and AI operations, then processing capability for specific functions is improved, but device complexity and resource utilization efficiency deteriorate
Solution Approach 1:
The patent implements a universal hardware acceleration platform that can dynamically configure and execute multiple wireless communication standards (5G, 4G, Wi-Fi) and AI operations through a single reconfigurable architecture. This multi-functional platform eliminates the need for separate dedicated hardware for each standard, thereby reducing device complexity while maintaining high processing capability across diverse workloads.
Solution Approach 2:
The system employs dynamic resource allocation and reconfiguration mechanisms that allow the hardware acceleration platform to adapt its structure and parameters in real-time based on the specific wireless standard or AI operation being executed. This dynamic adaptability enables optimal processing performance for each task while utilizing a single flexible hardware structure, resolving the contradiction between specialized processing capability and overall system complexity.
2Productivity
If dedicated hardware acceleration is implemented for specific wireless standards and AI operations, then processing capability for specific functions is improved, but resource utilization efficiency deteriorates
Solution Approach 1:
The universal hardware acceleration platform is designed to handle multiple wireless standards and AI operations through a single resource pool, ensuring that computational resources are shared and utilized efficiently across different workloads. This prevents resource idle time and improves overall resource utilization efficiency while maintaining high processing capability for each specific function.
Solution Approach 2:
The system dynamically adjusts operational parameters such as clock frequency, resource allocation, and configuration settings based on the specific task requirements. This parameter optimization ensures that the hardware acceleration platform operates at peak efficiency for each wireless standard or AI operation, maximizing resource utilization while maintaining high processing capability.
3Adaptability or versatility
If software-configurable converged modems are implemented, then adaptability to different wireless standards and AI operations is improved, but device complexity increases
Solution Approach 1:
The patent divides the software-configurable modem into modular functional blocks that can be independently configured and combined to support different wireless standards and AI operations. This segmentation allows the system to achieve high adaptability through software configuration of modular components, while the standardized interface and architecture reduce the overall system complexity compared to fully custom implementations.
Data Source
AI summary
With advanced compute capabilities and growing convergence of wireless standards and artificial intelligence (AI) applications, there is a requirement to run multiple wireless standards, e.g., 4G LTE, 5G NR and Wi-Fi, and AI on a single hardware together. Typical solutions include reserving some compute resources for specific wireless standards and a dedicated AI resource due to different precision compared to baseband processing. Typical solutions for signal processing with converged wireless standards and machine learning (ML) applications include having dedicated hardware acceleration and reserving some commutating or processing resources for specific wireless standards and a dedicated AI operation. Such approaches result in inefficient use of resources and lack of operation flexibility. The present patent document discloses embodiments to leverage commonalities in hardware acceleration for converged baseband and AI operators, thus achieving improved efficiency in power consumption and improved hardware resources utilization.