Parallel Mode Prediction Logic for Data Block Analysis
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Solution Overview
Problem
Conventional techniques for predicting the most commonly occurring values in data blocks, such as those using counters or hash tables, are inefficient and resource-intensive, wasting system resources and requiring excessive implementation overhead.
Innovation Solution
A novel technique that breaks down mode prediction into multiple parallel mode calculations per sub-blocks, using only repeatedly active comparators and reducing logic levels by bit slicing and concatenating results, allowing for efficient mode prediction with reduced hardware logic and improved timing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional techniques use counters or hash tables to build histograms for mode prediction, then prediction accuracy is maintained, but system resource consumption increases and implementation overhead becomes excessive
Solution Approach 1:
The patent divides the data block into multiple sub-blocks and performs mode prediction on each sub-block independently using simplified logic. This segmentation allows the system to avoid complex global histogram structures while maintaining prediction accuracy through localized analysis. Each sub-block uses its own counter array rather than a global hash table, reducing overall system complexity.
Solution Approach 2:
The patent extracts only the essential functionality needed for mode prediction by using direct-mapped counter arrays instead of full hash tables. This extraction removes unnecessary complexity while preserving the core prediction capability. The system takes out only the critical comparison and counting operations, discarding the overhead of hash function computation and table management.
2Loss of information
If conventional techniques use hash tables for mode prediction, then comprehensive data analysis is achieved, but hardware resources such as gates, areas, and power are wasted
Solution Approach 1:
The patent uses simple, disposable counter arrays that are easily initialized and discarded for each sub-block processing. These lightweight data structures require minimal hardware resources compared to persistent hash tables. The counters are reset and reused across processing cycles, avoiding the need for large, power-hungry memory structures while maintaining data analysis completeness.
3Productivity
If parallel mode calculations are implemented across sub-blocks, then processing speed increases, but hardware complexity increases
Solution Approach 1:
The patent merges the mode prediction logic across all sub-blocks into a unified comparison framework. Instead of implementing separate complex prediction mechanisms for each sub-block, the system combines them using a shared counter array structure and unified comparison logic. This merging achieves parallel processing speed while avoiding the proliferation of duplicate complex hardware logic.
Solution Approach 2:
The patent creates a universal counter array structure that serves multiple sub-blocks simultaneously. This multi-functional data structure handles mode prediction for all sub-blocks using the same hardware resources, eliminating the need for dedicated complex logic in each parallel processing unit. The universal structure achieves high productivity through resource sharing rather than through hardware multiplication.
Data Source
AI summary
A mechanism is described for facilitating efficient prediction of most commonly occurring values in data blocks in computing environments. An apparatus of embodiments, as described herein, includes one or more processors to perform parallel calculations on values associated with multiple sub-blocks of a data block, and predict, based on the parallel calculations, a most commonly-occurring value in the data block. The apparatus if further to classify the most commonly-occurring value as a mode value for one or more data types to be used with one or more applications.