Reference Data Update Streams for Low-Memory Embedded Software
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Solution Overview
Problem
Existing methods for updating embedded software in embedded systems are inefficient due to high memory usage and slow processing, as current Delta algorithms do not effectively leverage the small memory footprint and low-cycle processors of these systems.
Innovation Solution
A computer-implemented method that structures update data into multiple streams, with one stream containing patterns of instructions and another containing patterns of operands, optimized for compression using the LZ77 algorithm, which increases data similarity and reduces memory requirements, allowing for efficient updates in embedded systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing Delta algorithms are used for updating embedded software, then update functionality is provided, but memory usage is high and processing speed is slow
Solution Approach 1:
The patent segments update data into multiple streams organized by data characteristics and access patterns. This segmentation allows the system to process different types of data independently, optimizing memory usage and processing speed for each stream type, thereby resolving the contradiction between high memory usage and slow processing speed in existing Delta algorithms
Solution Approach 2:
The patent changes key parameters of the Delta algorithm by introducing configurable stream organization, compression ratios, and processing priorities. These parameter changes enable the algorithm to adapt to embedded system constraints, reducing memory footprint while improving update processing speed through optimized data handling
2Loss of substance
If update data is structured into multiple streams with instructions and operands separated, then compression efficiency improves by over 10%, but data structure complexity increases
Solution Approach 1:
The patent applies segmentation by dividing update data into distinct streams for instructions and operands, enabling independent compression optimization for each type. This segmentation achieves over 10% compression efficiency improvement while managing complexity through systematic organization rather than arbitrary structuring
Solution Approach 2:
The multi-stream data structure serves multiple functions simultaneously: it enables better compression, facilitates parallel processing, and allows selective updating of different data types. This multi-functionality justifies the increased structural complexity by delivering multiple benefits from a single organizational approach
Data Source
AI summary
A computer-implemented method is provided for updating reference data. The method includes a step of providing update data structured according to a given data structure. The data structure has at least two streams of data, wherein one of the streams includes patterns of instructions to a processor, and at least one other stream includes patterns of operands of the instructions. The operands and the instructions are designed for updating the reference data upon processing the update data at a processor. Also, a computer executable program code is provided, which is stored in a computer readable medium or in a signal, suitable for implementation of the method. Further, a terminal (e.g. a payment terminal or electronic funds transfer at point of sale) is provided, which includes a memory with the program code stored thereon.