Vector Lane Permutation Using Streaming Engines for DSP Throughput
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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 handling multiple data streams and performing vector permutations efficiently.
Innovation Solution
The implementation of a streaming engine within the DSP processor that includes two closely coupled streaming engines to manage data streams, along with instruction support for vector permutation, enabling all byte permute patterns and optimizing memory access through a system-on-chip architecture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional memory access methods are used for vector permutation, then memory bandwidth is limited and cache miss stalls increase, but implementing a streaming engine with vector permutation instructions increases device complexity
Solution Approach 1:
The streaming engine is implemented as a separate functional unit within the DSP processor, distinct from the traditional memory access pathways. This segmentation allows the streaming engine to handle vector permutation operations independently, utilizing dedicated control storage locations and permutation components that operate in parallel with the main processor, thereby increasing memory bandwidth without significantly impacting overall device complexity.
Solution Approach 2:
The streaming engine acts as an intermediary between the processor and the memory system, specifically handling vector permutation operations. By introducing this intermediate layer with specialized instructions and control mechanisms, the system achieves efficient memory access patterns for vector operations without requiring fundamental changes to the entire memory subsystem, thus improving productivity with controlled complexity increase.
2Productivity
If scalar operations are used for loop maintenance in real-time data processing, then processing efficiency is reduced, but optimizing for vector operations increases instruction complexity
Solution Approach 1:
The streaming engine implements dynamic vector permutation capabilities that adapt to different data processing requirements in real-time. The control storage locations can be configured with different permute control inputs for various lanes, allowing the system to dynamically optimize processing efficiency for different vector operations without requiring a complete redesign of the instruction set architecture.
Solution Approach 2:
The streaming engine with vector permutation instructions provides multi-functional capabilities that can handle various real-time data processing tasks including audio, video, and sensor data. By creating a universal vector processing unit that can perform multiple operations (permutation, memory access, data transformation), the system improves processing efficiency across different applications without proportionally increasing instruction complexity.
Data Source
AI summary
A method is provided that includes performing, by a processor in response to a vector permutation instruction, permutation of values stored in lanes of a vector to generate a permuted vector, wherein the permutation is responsive to a control storage location storing permute control input for each lane of the permuted vector, wherein the permute control input corresponding to each lane of the permuted vector indicates a value to be stored in the lane of the permuted vector, wherein the permute control input for at least one lane of the permuted vector indicates a value of a selected lane of the vector is to be stored in the at least one lane, and storing the permuted vector in a storage location indicated by an operand of the vector permutation instruction.