Page Data Decompression Pipeline for Faster App Context Switching
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Solution Overview
Problem
Existing user devices face inefficiencies in decompressing page data associated with multiple applications, leading to suboptimal memory usage and performance, particularly during context-switching events.
Innovation Solution
A system and method for decompressing page data in User Equipment (UE) involving detection of context-switching events, prefetching compressed data, parsing and re-encoding tokens based on data format, and generating decompressed data through an output handler, with mechanisms to handle varying data formats and memory constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If compressed data is stored in DRAM for background applications, then memory usage is optimized, but decompression speed and system performance deteriorate during context-switching events
Solution Approach 1:
The system performs preliminary actions by detecting context-switching events before they occur and prefetching compressed data into a memory buffer in advance. This allows the decompression process to begin immediately when the application is activated, eliminating decompression latency and improving system performance during context-switching.
Solution Approach 2:
The decompression process is segmented into multiple independent stages: token parsing, format identification, re-encoding, and decompression. Each stage processes data independently and can be executed in parallel, significantly improving decompression speed while maintaining memory efficiency.
2Ease of manufacture
If traditional decompression methods are used, then implementation simplicity is maintained, but processing efficiency and latency are suboptimal
Solution Approach 1:
Compressed data is prefetch ed into a memory buffer before actual decompression is needed. This preliminary action ensures data is readily available when context-switching occurs, eliminating wait time and reducing processing latency without complicating the implementation.
Solution Approach 2:
The system maintains continuous useful action by implementing a pipeline architecture where token parsing, format identification, re-encoding, and decompression occur in continuous overlapping stages. This eliminates idle time between operations and minimizes processing latency.
3Loss of time
If compressed data is stored in memory buffer, then decompression latency is reduced, but memory complexity increases
Solution Approach 1:
The memory system is segmented into distinct components: input memory for storing compressed data, a memory buffer for prefetching, and output memory for storing decompressed data. This segmentation allows each component to be optimized independently and simplifies memory management while reducing decompression latency.
Solution Approach 2:
A memory buffer is introduced as an intermediary between input memory and the decompression processor. This buffer acts as a staging area that decouples data fetching from decompression operations, reducing latency without significantly increasing overall memory complexity.
4Adaptability or versatility
If multiple data formats are handled, then compatibility with different applications is improved, but processing complexity increases
Solution Approach 1:
The processing logic is segmented into modular stages: token parsing, format identification, re-encoding, and decompression. Each stage handles specific tasks independently, making it easier to support multiple data formats without increasing overall processing complexity. The modular architecture allows format-specific logic to be isolated and managed separately.
Solution Approach 2:
The system handles multiple data formats by detecting the specific format of incoming compressed data and dynamically adjusting processing parameters accordingly. This includes format identification through token analysis and adaptive re-encoding strategies that modify processing behavior based on the detected format, maintaining compatibility without requiring complex hard-coded logic for each format.
Data Source
AI summary
A method of performing decompression of page data associated with a plurality of applications installed in a User Equipment (UE) includes detecting a context-switching event between at least one first application and at least one second application and prefetching compressed data including a plurality of tokens, parsing each of the plurality of tokens of the prefetched compressed data from the memory buffer and identifying a data format of each of the plurality of tokens, re-encoding each of the parsed tokens and temporarily storing the re-encoded parsed tokens in an instruction queue of an output handler, generating decompressed data, continuously collecting the generated decompressed data into an output packer, and performing a writing operation to write a single line of the data from the collected decompressed data into the output memory.