GPU Selective Two-Level Binning for Power and Thermal Balance
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Solution Overview
Problem
In battery-powered devices, there is a tradeoff between GPU performance and battery life/thermals, with existing technologies struggling to adaptively balance performance and power consumption while maintaining efficient rendering processes.
Innovation Solution
A selective two-level binning architecture for GPUs that dynamically selects between two-level and single-level binning modes based on real-time performance data, such as thermal and power characteristics, allowing for runtime adaptation of command buffer workloads to optimize rendering efficiency and power usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If two-level binning mode is used to improve rendering efficiency and GPU performance, then productivity is improved, but use of energy increases and thermals worsen
Solution Approach 1:
The system dynamically switches between two-level binning mode and single-level binning mode based on real-time performance metrics (temperature, power consumption, frame rate). The binning mode is not fixed but adapts to changing operational conditions, allowing the GPU to optimize between performance and power efficiency dynamically throughout operation.
Solution Approach 2:
The invention changes the binning mode parameter (from single-level to two-level or vice versa) based on monitored performance parameters. When temperature or power consumption exceeds thresholds, the system changes the binning configuration to reduce power usage while maintaining acceptable performance, directly applying parameter changes to resolve the contradiction.
2Productivity
If two-level binning mode is selected to enhance rendering efficiency, then productivity is improved, but temperature increases
Solution Approach 1:
The binning mode dynamically adapts to thermal conditions. When GPU temperature exceeds a predefined threshold, the system automatically switches from two-level binning mode to single-level binning mode, reducing thermal generation while maintaining acceptable rendering performance. This dynamic response to thermal conditions resolves the contradiction between performance and temperature.
Solution Approach 2:
The system continuously monitors temperature as feedback and uses this information to adjust the binning mode. The temperature threshold triggers a feedback loop that switches the binning configuration, creating a closed-loop control system that maintains temperature within acceptable ranges while optimizing performance.
3Use of energy by moving object
If single-level binning mode is used to reduce power consumption, then use of energy is improved, but productivity decreases
Solution Approach 1:
The system dynamically selects between single-level and two-level binning modes based on operational context. During high-performance需求的 periods, two-level mode provides superior rendering efficiency. During power-sensitive periods, single-level mode reduces power consumption. The dynamic switching resolves the contradiction by allowing each mode to excel in its appropriate context.
Solution Approach 2:
The invention changes the binning mode parameter based on power consumption thresholds and performance requirements. When power consumption exceeds thresholds or performance requirements are met, the system changes to single-level binning to reduce power usage. This parameter change allows the system to optimize power efficiency without permanently sacrificing performance capability.
4Adaptability or versatility
If adaptive binning mode selection is implemented to balance performance and power, then adaptability is improved, but device complexity increases
Solution Approach 1:
The invention segments the binning control into distinct modes (single-level and two-level) with clear transition conditions. Rather than implementing a continuously complex adaptive system, the control is segmented into discrete states with well-defined thresholds for switching. This segmentation reduces complexity while maintaining adaptability.
Solution Approach 2:
The system uses simple parameter changes (mode switching based on thresholds) rather than complex algorithms to achieve adaptability. The binning mode parameter is changed based on straightforward comparisons of temperature, power consumption, and performance metrics, avoiding the need for complex machine learning or optimization algorithms while still providing adaptive behavior.
Data Source
AI summary
Systems and methods related to run-time selection of a render mode in which to execute command buffers with a graphics processing unit (GPU) of a device based on performance data corresponding to the device are provided. A user mode driver (UMD) or kernel mode driver (KMD) executed at a central processing unit (CPU) selects abinning mode based on whether performance data that includes sensor data or performance counter data indicates that an associated binning condition or override condition has been met. The UMD or the KMD causes pending command buffers to be patched to execute in the selected binning mode based on whether the binning mode is enabled or disabled.