Vector Processor Parallel Data Stream Alignment
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Solution Overview
Problem
Serial or sequential processing of heterogeneous data streams is inefficient and slow, particularly in high-bandwidth communication systems like 4G, 5G, and mmWave, where data streams with different formats and compression schemes need to be re-formed or combined, leading to insufficient performance in handling increased data rates.
Innovation Solution
A vector processor with multiple parallel processing units and grouping memory that writes and reads data streams in parallel, using a bit-level Benes network to align and rearrange data samples into homogeneous streams, reducing the overhead of multiple loops and enhancing processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If serial or sequential processing is used to re-form homogeneous streams from heterogeneous data streams, then processing accuracy is maintained, but processing speed and efficiency deteriorate
Solution Approach 1:
The processing system is segmented into multiple parallel processing units, each capable of independently handling different data streams. This segmentation allows simultaneous processing of multiple homogeneous streams from heterogeneous input, dramatically improving processing speed while maintaining accuracy through dedicated processing paths for each stream type
Solution Approach 2:
The patent transitions from one-dimensional sequential processing to multi-dimensional parallel processing by introducing multiple processing units operating simultaneously. This dimensional change enables the system to handle heterogeneous data streams with different formats and compression schemes in parallel, resolving the contradiction between speed and complexity
2Productivity
If multiple loops are used to process different data streams with different formats and compression schemes, then processing completeness is ensured, but processing overhead increases
Solution Approach 1:
Multiple processing loops are merged into a unified parallel processing architecture where multiple processing units operate simultaneously on different data streams. This merging eliminates the sequential overhead of multiple loops while ensuring complete processing of all stream types through coordinated parallel execution
Solution Approach 2:
The parallel processing architecture enables continuous useful action by eliminating idle time between processing different data streams. All processing units operate continuously and simultaneously, removing the gaps and overhead associated with sequential loop execution while maintaining complete processing coverage
Data Source
AI summary
A vector processor includes a grouping memory functional unit coupled to grouping memory having multiple bins. The vector processor also includes a bitformatting functional unit that performs bit-level data arrangements using any suitable technique or network, such as a Benes network. The vector processor receives and reads an input vector of data that includes portions (e.g., bits) of multiple data streams, and writes each portion corresponding to a respective data stream to a respective bin in parallel using the bitformatting functional unit to align the data. The vector processor also or alternatively receives and reads multiple outgoing data streams, writes portions of the data streams in respective bins of the grouping memory, and intersperses the portions in an outgoing vector of data in parallel, using the bitformatting functional unit to align the data.