Instruction-Aware Arithmetic Coding for DSP Code Compression
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Solution Overview
Problem
Existing code compression techniques for digital signal processors (DSPs) are inefficient, leading to larger code memory sizes and increased footprint in devices such as augmented-reality glasses and virtual-reality headsets.
Innovation Solution
The method involves dividing binary code into chunks, clustering similar chunks, and applying a combination of arithmetic coding and Lempel-Ziv compression techniques tailored to each cluster, with iterative evaluations to optimize compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If existing code compression techniques are used for DSPs, then the compression process is simpler, but the code memory size becomes larger and DSP footprint increases
Solution Approach 1:
The patent divides binary code into multiple chunks and further segments each chunk into instruction components (opcode, operands, etc.). This segmentation allows application of different compression techniques to different segments, achieving better overall compression ratios while managing complexity through structured processing.
Solution Approach 2:
The patent applies different compression techniques (arithmetic coding for certain instruction types, Lempel-Ziv for others) based on the local characteristics of each instruction chunk. This local quality approach optimizes compression efficiency for specific instruction patterns without requiring complete redesign of the entire compression system.
2Quantity of substance
If code memory size is reduced through compression, then device footprint is reduced, but compression efficiency becomes insufficient with existing techniques
Solution Approach 1:
The patent changes the parameters of the compression process by using arithmetic coding with dynamically adjusted probability models specific to DSP instruction sets. It also modifies the Lempel-Ziv algorithm parameters to better match the statistical properties of compiled code, thereby improving compression efficiency and reducing code memory size.
Solution Approach 2:
The patent creates a composite compression approach by combining arithmetic coding and Lempel-Ziv algorithms into a hybrid system. This composite technique leverages the strengths of both methods—arithmetic coding for high-compression scenarios and Lempel-Ziv for pattern-matching scenarios—achieving superior overall compression efficiency.
3Productivity
If arithmetic coding and Lempel-Ziv techniques are combined and tailored to instruction clusters, then compression efficiency improves, but the compression process becomes more complex
Solution Approach 1:
The patent introduces dynamic selection of compression techniques based on the characteristics of each instruction cluster. The system dynamically determines whether to apply arithmetic coding, Lempel-Ziv, or a combination, based on real-time analysis of instruction patterns. This dynamic approach improves compression efficiency while managing complexity through adaptive decision-making rather than static complex processing.
Data Source
AI summary
A computer-implemented method may include dividing, by a computer processor, binary code into a plurality of chunks, wherein the binary code includes a plurality of instructions. The method may additionally include clustering, by the computer processor, similar chunks of the plurality of chunks. The method may also include performing, by the computer processor, compression of the binary code, the compression being tailored to one or more clusters of the similar chunks. Various other methods, systems, and computer-readable media are also disclosed.