Hardware Accelerator for Selecting Top N Data Elements
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Solution Overview
Problem
Current computing applications face inefficiencies in selecting data elements from larger datasets, as they often require sorting the entire input data set, which consumes substantial hardware resources and computation time.
Innovation Solution
A selection accelerator is introduced that increments bin counters based on score values, determines a cumulative sum, and identifies a threshold score to generate an output data set, allowing for efficient selection of top N data elements without needing to process the entire dataset.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the entire input data set is sorted to select data elements, then the selection accuracy is improved, but the computation time and hardware resources increase substantially
Solution Approach 1:
The patent divides the data selection process into two independent stages: (1) a reduction stage that segments the input data into a smaller candidate set using sampling and filtering techniques, and (2) a selection stage that applies precise sorting only to this reduced candidate set. This segmentation maintains selection accuracy while dramatically reducing computation time by avoiding full dataset sorting.
Solution Approach 2:
The patent applies partial sorting action by performing sorting operations only on a subset of data elements (the reduced candidate set) rather than the entire input dataset. This partial action is sufficient to achieve the required selection accuracy while avoiding the excessive computational cost of complete dataset sorting.
2Measurement precision
If the entire input data set is sorted to select data elements, then the selection accuracy is improved, but the hardware resources increase substantially
Solution Approach 1:
The patent segments the data processing workload by separating reduction operations (performed on full dataset) from selection operations (performed on reduced set). This allows hardware resources to be optimized for each stage independently, reducing overall device complexity while maintaining selection accuracy through the two-stage approach.
Solution Approach 2:
The patent applies partial sorting action by performing sorting operations only on a subset of data elements (the reduced candidate set) rather than the entire input dataset. This partial action is sufficient to achieve the required selection accuracy while avoiding the excessive computational cost of complete dataset sorting.
3Reliability
If traditional sorting methods are used to select top N data elements, then all data elements are processed thoroughly, but the productivity decreases
Solution Approach 1:
The patent divides the data selection process into two independent stages: (1) a reduction stage that segments the input data into a smaller candidate set using sampling and filtering techniques, and (2) a selection stage that applies precise sorting only to this reduced candidate set. This segmentation maintains selection accuracy while dramatically reducing computation time by avoiding full dataset sorting.
Solution Approach 2:
The patent applies partial sorting action by performing sorting operations only on a subset of data elements (the reduced candidate set) rather than the entire input dataset. This partial action is sufficient to achieve the required selection accuracy while avoiding the excessive computational cost of complete dataset sorting.
Data Source
AI summary
A processor may include a plurality of processing elements and a hardware accelerator for selecting data elements. The hardware accelerator may: access an input data set comprising a set of data elements, each data element having a score value; increment bin counters based on the score values of the set of data elements, each bin counter to count a number of data elements with an associated score value; determine a cumulative sum of count values for a sequence of bin counters, the sequence beginning with a first bin counter of the plurality of bin counters; identify a second bin counter in the sequence of bin counters at which the cumulative sum reaches a selection quantity N; and generate an output data set based on a comparison of the set of data elements to a threshold score associated with the second bin counter.