Compressed STA Vectors for Low-Memory Multi-Corner Timing Analysis
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Solution Overview
Problem
Current vector-based static timing analysis (STA) techniques face significant challenges with large memory footprints and prohibitive runtime overhead due to repeated compression and decompression of data, especially in deep submicron technologies where multi-corner analysis is complex and computationally demanding.
Innovation Solution
The method involves quantizing and compressing data sets for STA by generating vectors with base values and offset values, applying incremental and differential compression, and performing operations directly on these compressed data without de-quantization or decompression, reducing memory footprint and improving runtime efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data compression techniques are applied to reduce memory footprint, then memory usage is reduced, but processing requires repeated compression and decompression which increases runtime overhead
Solution Approach 1:
The patent applies compression algorithms to the data vectors before the STA processing begins. The compressed data is then processed directly through the timing analysis operations without requiring decompression at each access point. This preliminary compression action eliminates the repeated compress-decompress cycles that previously caused runtime overhead, as the compression is performed once upfront rather than repeatedly during processing.
2Quantity of substance
If quantization is applied to reduce data size, then memory footprint is reduced, but repeated de-quantization and re-quantization during STA operations increases runtime overhead
Solution Approach 1:
The patent performs quantization on the timing data vectors before the STA processing begins. The quantized data maintains sufficient precision for timing analysis while occupying significantly less memory. By completing the quantization action beforehand, the system avoids repeated de-quantization and re-quantization operations during the STA processing, thereby eliminating the runtime overhead that would otherwise be incurred by these repeated conversions.
3Measurement precision
If multi-corner analysis is performed to account for process, voltage, and temperature variations, then timing accuracy is improved, but computational complexity and memory requirements increase
Solution Approach 1:
The patent represents timing data from multiple corners (different process, voltage, and temperature conditions) as vectors of parameters rather than separate full datasets. By changing the representation from complete corner-specific data to parameter vectors, the system can perform multi-corner analysis with reduced computational complexity and memory requirements while maintaining timing accuracy across all corners.
Solution Approach 2:
The patent creates a universal data structure (vectors of timing parameters) that can represent and process data from multiple corners simultaneously. This universal representation allows the same processing operations to be applied across all corners without requiring separate processing pipelines for each corner, thereby reducing overall computational complexity while maintaining the ability to analyze timing variations across process, voltage, and temperature conditions.
Data Source
AI summary
In a STA method, after accessing data sets regarding the IC, vectors of the data sets for STA can be generated. Each vector can include a base value and a plurality of tokens, wherein each token is quantized. For each vector, the data of the vector can be adjusted. Adjusting can include shifting a predetermined token to zero and adjusting the base value and remaining token values based on the shifting. Incremental compression can be applied within the vector by storing each token value as a difference versus its previous token value. Differential compression can then be applied by storing each token value as a difference versus a corresponding token value in a predetermined reference vector. A resulting vector can be stored. At this point, an operation for STA can be performed using multiple resulting vectors without de-quantizing or decompressing.