Vector Lane Sorting for Bitonic DSP Memory Scheduling
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Solution Overview
Problem
Digital signal processors face challenges in efficiently managing memory bandwidth and scheduling for real-time data processing, particularly in applications requiring predictable but non-sequential input data patterns, which can lead to performance issues if transformed data is not output on schedule.
Innovation Solution
The implementation of a streaming engine within the DSP processor that includes two closely coupled streaming engines to manage data streams, with features like address generation, data formatting, and storage, along with instruction support for vector sorting, to improve memory bandwidth and data scheduling, allowing for multi-dimensional memory accesses and reduced cache miss stalls.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a DSP processor manages memory bandwidth and scheduling for real-time data processing, then processing capability is improved, but memory access efficiency deteriorates due to non-sequential input data patterns
Solution Approach 1:
The patent segments the data processing workflow by introducing a streaming engine as a separate module that handles memory fetch tasks independently from the main DSP processor. This segmentation allows the processor to focus on computation while the streaming engine manages memory access patterns, thereby improving both processing capability and memory access efficiency simultaneously
Solution Approach 2:
The streaming engine acts as an intermediary between memory and the DSP processor. It generates addresses, formats data, and manages memory bandwidth independently, mediating the interaction between the processor and memory systems. This intermediary approach resolves the contradiction by allowing the processor to maintain high processing capability while the streaming engine optimizes memory access patterns for non-sequential data
2Device complexity
If the processor handles memory fetch tasks directly, then control is simplified, but cache miss stalls increase reducing overall performance
Solution Approach 1:
The patent extracts the memory fetch task management function from the main DSP processor and places it in a dedicated streaming engine. This extraction removes the time-consuming cache miss handling from the critical processor path, significantly reducing cache miss stalls. The processor maintains simplified control by issuing high-level stream commands to the streaming engine, which autonomously handles the complex memory fetch operations
3Ease of manufacture
If scalar operations are used for data processing, then implementation is simpler, but processing efficiency deteriorates for vector operations
Solution Approach 1:
The streaming engine implements a universal data formatting and management system that handles both scalar and vector operations through the same infrastructure. The engine can process data in different formats and dimensions without requiring separate implementation paths, maintaining implementation simplicity while achieving high processing efficiency for vector operations through parallel data preparation and streaming
Data Source
AI summary
A method is provided that includes performing, by a processor in response to a vector sort instruction, sorting of values stored in lanes of the vector to generate a sorted vector, wherein the values in a first portion of the lanes are sorted in a first order indicated by the vector sort instruction and the values in a second portion of the lanes are sorted in a second order indicated by the vector sort instruction; and storing the sorted vector in a storage location.