Adaptive Exponent Bias Compression for Floating Point Precision
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data compression methods for floating point values face challenges in balancing precision and performance, as reducing precision can lead to erroneous results below a certain threshold.
Innovation Solution
A method and system that adaptively compress floating point values by accumulating the distribution of exponent values and adjusting the compressed exponent bit-width and bias accordingly, allowing for dynamic optimization of precision and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the number of mantissa bits is reduced to improve performance, then processing speed increases, but floating point precision deteriorates
Solution Approach 1:
The patent dynamically changes the exponent bit-width parameter based on the accumulated distribution of exponent values. By adjusting the compression level adaptively, the system optimizes the balance between processing speed and precision for different data characteristics, allowing higher compression (fewer bits) when appropriate while maintaining accuracy when needed.
Solution Approach 2:
The system transitions from static precision settings to dynamic adaptation. A statistics circuit accumulates exponent distribution over time, and a bias adjustment circuit dynamically adjusts the compression parameters based on this accumulated data, enabling the system to adapt its precision-performance tradeoff in real-time based on actual data characteristics.
2Reliability
If full precision floating point values are maintained to preserve accuracy, then computation accuracy improves, but memory and computation requirements increase
Solution Approach 1:
The patent dynamically adjusts the exponent bit-width parameter based on accumulated statistics of the data distribution. This allows the system to use fewer bits (reducing memory and computation requirements) when the data characteristics permit, while maintaining full precision when accuracy is critical, thus resolving the contradiction between resource usage and accuracy.
Solution Approach 2:
Instead of always maintaining full precision, the system applies partial precision compression adaptively. The compression level is adjusted to be just sufficient for the current data characteristics, avoiding excessive precision when not needed and thereby reducing memory and computation requirements while maintaining adequate accuracy.
3Quantity of substance
If exponent compression is applied to reduce data size, then memory efficiency improves, but exponent representation accuracy deteriorates
Solution Approach 1:
The system dynamically adjusts the exponent compression level based on accumulated distribution statistics. The bias adjustment circuit modifies the compression parameters in response to the actual exponent value distribution, ensuring that compression accuracy is optimized for the specific data being processed rather than using a fixed compression scheme.
Solution Approach 2:
The statistics circuit continuously accumulates exponent distribution information, providing feedback to the bias adjustment circuit. This feedback loop allows the system to learn from the actual data characteristics and adjust the compression parameters accordingly, improving exponent representation accuracy while maintaining compression efficiency.
Data Source
AI summary
Approaches for compressing exponents of floating point values include accumulating a distribution of values of exponents of the first set of floating point values, and compressing the exponents of the first set of floating point values into a compressed exponent bit-width as a function of a compressed exponent bias. The compressed exponent bit-width and the compressed exponent bias are adjusted based on the distribution of values of exponents of the first set of floating point values. The distribution of values of exponents of the first set of floating point values is accumulated with values of exponents of a second set of floating point values that is input in subsequent time period. The exponents of second set of floating point values are compressed into the compressed exponent bit-width as a function of the compressed exponent bias after the adjusting of the compressed exponent bit-width and the compressed exponent bias.