Processor Compression Instructions for Faster Low-Power Entropy Coding
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Solution Overview
Problem
Current compression/decompression algorithms in processors are inefficient in terms of performance and power consumption, particularly in portable devices, due to their complexity and high latency, which affects the compression ratio and overall system performance.
Innovation Solution
The implementation of compression assist instructions in processors, such as the Finite State Entropy (FSE) algorithm, which utilizes vector operations and bit stack management through specific instructions like 'top' and 'pop' to enhance parallel processing and reduce power consumption during compression and decompression tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If complex compression algorithms are used to improve compression ratio, then compression effectiveness is improved, but performance and power consumption worsen
Solution Approach 1:
The compression algorithm is divided into distinct functional stages (entropy decoding, bitstream processing, symbol decoding) that can be independently optimized and executed. This segmentation allows complex compression tasks to be broken down into manageable operations that can be parallelized or optimized separately, improving overall performance while maintaining compression effectiveness.
Solution Approach 2:
The patent employs parameter changes by using variable bit-width operations and dynamic state transitions in the Finite State Entropy decoder. The decoder adapts its operation based on input characteristics, changing processing parameters on-the-fly to optimize for both compression ratio and speed, rather than using fixed complex algorithms.
2Manufacturing precision
If complex compression algorithms are used to improve compression ratio, then compression effectiveness is improved, but power consumption worsens
Solution Approach 1:
The compression system performs self-optimization by using the input data characteristics to dynamically adjust decoding parameters and state transitions. The Finite State Entropy decoder automatically adapts its behavior based on the compressed data being processed, eliminating the need for external control logic and reducing overall system power consumption while maintaining high compression ratios.
3Adaptability or versatility
If software-based compression is used to achieve flexibility, then adaptability is improved, but performance and latency worsen
Solution Approach 1:
The patent replaces traditional software-based compression mechanisms with hardware-accelerated Finite State Entropy decoding circuits. This substitution maintains the flexibility and adaptability of software algorithms while achieving the high-speed performance of hardware implementation, effectively bridging the gap between flexibility and performance through a hybrid approach.
4Ease of operation
If traditional compression instructions are used, then ease of operation is maintained, but productivity and speed worsen
Solution Approach 1:
The compression assist instructions are designed to be universally applicable to various compression formats and algorithms while providing hardware-accelerated performance. The same instruction set can handle different entropy decoding schemes and bitstream formats, maintaining ease of operation across multiple use cases while delivering high-speed performance through dedicated hardware circuits.
Data Source
AI summary
In an embodiment, a processor supports one or more compression assist instructions which may be employed in compression software to improve the performance of the processor when performing compression/decompression. That is, the compression/decompression task may be performed more rapidly and consume less power when the compression assist instructions are employed then when they are not. In some cases, the cost of a more effective, more complex compression algorithm may be reduced to the cost of a less effective, less complex compression algorithm.