Hybrid Low Power Homogeneous GPU Architecture for Thermal Management
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Solution Overview
Problem
As integrated circuit fabrication advances, the increasing number of components on a single chip leads to higher power consumption and heat generation, limiting device usage and longevity, especially in battery-powered devices, and existing graphics processing units face inefficiencies in parallel data processing.
Innovation Solution
The development of hybrid low power homogeneous graphics processing units, which incorporate efficient power management techniques and programmable computational units to optimize parallel processing through SIMT architectures and dedicated circuitry for graphics operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If additional components are integrated onto a single silicon substrate to improve functionality, then device capability is enhanced, but power consumption and heat generation increase
Solution Approach 1:
The graphics processing unit is divided into multiple independent processing clusters, each capable of autonomous operation. This segmentation allows the system to activate only the necessary number of clusters based on workload requirements, reducing overall power consumption while maintaining the capability to handle complex graphics tasks when needed.
Solution Approach 2:
The system implements dynamic power management by adjusting the operational state of processing clusters in real-time based on workload demands. This dynamic approach allows the GPU to transition between different power states, optimizing the balance between device capability and power consumption rather than operating at fixed performance levels.
2Adaptability or versatility
If additional components are integrated onto a single silicon substrate to improve functionality, then device capability is enhanced, but heat generation increases
Solution Approach 1:
By segmenting the GPU into multiple independent processing clusters with dedicated memory resources, the system can distribute thermal load across separate physical units. This spatial distribution of computational work reduces heat concentration in any single area, allowing higher overall device capability without proportional increases in localized temperature.
Solution Approach 2:
The system employs periodic activation and deactivation of processing clusters based on workload requirements. This periodic operation allows thermal management by cycling clusters on and off, preventing continuous heat generation while maintaining the capability to process graphics tasks when activated.
3Productivity
If fixed function computational units are used to process graphics data, then processing efficiency is improved, but adaptability to different operations is reduced
Solution Approach 1:
Each processing cluster is designed as a universal computational unit capable of executing multiple types of graphics operations including vertex processing, fragment processing, and geometry processing. This multi-functionality allows fixed function units to adapt to different operations through software configuration rather than hardware reconfiguration, maintaining processing efficiency while enabling operational flexibility.
Solution Approach 2:
The system dynamically configures the function of processing clusters through programmable interfaces, allowing the same hardware to adapt to different graphics operations. This dynamic reconfiguration capability enables fixed function units to perform multiple roles based on runtime requirements, combining the efficiency of dedicated hardware with the flexibility of programmable systems.
4Productivity
If processing clusters are activated to improve graphics processing performance, then processing speed is enhanced, but power consumption increases
Solution Approach 1:
The GPU architecture segments processing functionality into multiple independent clusters that can be activated independently. This allows the system to scale power consumption linearly with performance requirements by activating only the necessary number of clusters, rather than powering the entire GPU at full capacity regardless of actual workload demands.
Solution Approach 2:
The system implements partial activation of processing clusters based on actual workload requirements. Rather than activating all available processing units, the system activates only the partial number needed to handle the current graphics processing task, optimizing the trade-off between processing speed and power consumption by avoiding excessive action.
Data Source
AI summary
In an example, an apparatus comprises a plurality of execution units comprising at least a first type of execution unit and a second type of execution unit and logic, at least partially including hardware logic, to analyze a workload and assign the workload to one of the first type of execution unit or the second type of execution unit. Other embodiments are also disclosed and claimed.