Adaptive Compression Resource Matching in Semiconductor Hardware
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Solution Overview
Problem
There is a need for efficient data compression and decompression techniques in semiconductor devices, particularly in mobile environments, where existing methods lack the ability to adapt to varying resource availability and context changes.
Innovation Solution
A semiconductor device and method that acquires resource and context information to dynamically select the most suitable compression algorithm and hardware resource for data compression and decompression, utilizing a combination of heterogeneous hardware resources to optimize performance and power usage based on real-time conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single hardware resource is used for compression/decompression, then device complexity is reduced, but adaptability to different compression algorithms and workloads deteriorates
Solution Approach 1:
The patent implements a resource manager that dynamically assigns different hardware resources (CPU, GPU, NPU, DSP) to execute compression algorithms based on workload characteristics and resource availability. This multi-functional approach allows a single hardware platform to adaptively handle various compression tasks using the most suitable processor for each specific algorithm and data type, thereby achieving both hardware resource efficiency and algorithmic versatility without requiring dedicated hardware for each compression scenario.
Solution Approach 2:
The system employs dynamic resource selection where the resource manager continuously monitors hardware resource states and workload characteristics to determine the optimal hardware resource for each compression task. This dynamic assignment allows the system to adapt to changing conditions in real-time, selecting the most appropriate hardware resource (CPU, GPU, NPU, or DSP) based on current power availability, performance requirements, and algorithm characteristics, thus resolving the contradiction between fixed hardware structure and adaptive functionality.
2Adaptability or versatility
If multiple hardware resources are used for compression/decompression, then adaptability and performance are improved, but device complexity increases
Solution Approach 1:
The patent introduces a resource manager as an intermediary layer between the compression algorithms and the heterogeneous hardware resources (CPU, GPU, NPU, DSP). This resource manager acts as a mediator that receives compression requests, evaluates workload characteristics and hardware resource states, and dynamically assigns the most suitable hardware resource for execution. By centralizing the decision-making logic in the resource manager, the system handles the complexity of multi-resource coordination without requiring each compression algorithm to be aware of hardware specifics, thus improving adaptability while managing complexity through a unified control mechanism.
Solution Approach 2:
The system dynamically changes operational parameters (such as power consumption thresholds, performance targets, and resource availability flags) to determine optimal hardware resource selection. The resource manager monitors these parameters in real-time and adjusts hardware resource assignment accordingly, allowing the system to adapt to changing power conditions, thermal states, and performance requirements without requiring complex hardwired control logic for each scenario.
3Use of energy by moving object
If dynamic resource selection is implemented, then power efficiency and performance are improved, but control complexity increases
Solution Approach 1:
The resource manager implements a feedback mechanism that continuously monitors hardware resource states (power consumption, utilization, thermal conditions) and workload characteristics, then uses this information to dynamically adjust hardware resource selection for compression tasks. This closed-loop control allows the system to optimize power efficiency by selecting low-power hardware resources when appropriate while maintaining performance when needed, with the feedback loop automatically adapting to changing conditions without requiring complex manual intervention or predetermined configurations.
Data Source
AI summary
Disclosed is an operating method of a semiconductor device, including acquiring resource information on a plurality of hardware resources, receiving a compression request or a decompression request for data, acquiring context information on the semiconductor device, in response to receiving the compression request or the decompression request for the data, selecting a compression algorithm for compressing or decompressing the data, based on the context information, selecting, among the plurality of hardware resources, a hardware resource for performing the selected compression algorithm, based on the acquired resource information, and compressing or decompressing the data using the selected compression algorithm and the selected hardware resource.