Operation Fusion in Nested Meta-Pipeline Loops for CGRA Throughput
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Solution Overview
Problem
Optimizing compute graphs for the configurable units of coarse-grained reconfigurable architectures (CGRAs) remains a challenge, particularly in efficiently executing massively parallel computations required for machine learning and artificial intelligence tasks.
Innovation Solution
A method for improving throughput in reconfigurable computing systems involves detecting nested meta-pipeline loops and fusing common operations within the inner meta-pipeline loop, including accumulator and temporal operations, to enhance parallel processing in CGRAs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If nested meta-pipeline loops are executed separately in CGRAs, then the implementation complexity is reduced, but the throughput and execution efficiency deteriorate
Solution Approach 1:
The patent merges the execution of outer and inner meta-pipeline loops by fusing common operations between them. Instead of executing loops separately, the system identifies operations that are common to both loops and combines their execution, allowing data to flow directly between loop iterations without intermediate storage, thereby increasing throughput while maintaining manageable complexity through systematic fusion rules.
Solution Approach 2:
The patent applies nested loop fusion where the inner meta-pipeline loop is nested within the outer meta-pipeline loop structure. By identifying that the inner loop is contained within the outer loop's iteration space, the system can fuse operations across both loops, allowing inner loop operations to be executed as part of the outer loop's execution flow, improving throughput through better resource utilization.
2Productivity
If common operations are fused between nested loops, then the throughput is improved, but the operation complexity increases
Solution Approach 1:
The patent performs preliminary analysis to identify common operations between nested loops before execution. The system pre-determines which operations can be fused by analyzing the loop structures and data dependencies, preparing fusion plans in advance. This preliminary identification reduces the complexity burden during actual execution by having fusion decisions already made, allowing throughput improvement without overwhelming operational complexity.
Solution Approach 2:
The patent applies operation fusion selectively rather than universally. Instead of fusing all operations between loops, the system identifies specific local regions where operations are common and fuses only those particular operations. This localized approach to fusion improves throughput in critical paths while avoiding the complexity increase that would result from fusing all operations throughout the entire loop structure.
3Duration of action of moving object
If nested meta-pipeline loops are executed without fusion, then the implementation is simpler, but the execution time increases
Solution Approach 1:
The patent enables continuous execution flow by fusing operations between nested loops, eliminating idle periods and intermediate storage operations. When operations are fused, data flows continuously from one operation to the next without waiting for loop boundaries or intermediate buffer transfers. This continuity maintains useful action throughout the execution process, reducing total execution time and improving throughput simultaneously.
Data Source
AI summary
A method for improving throughput in a reconfigurable computing system includes detecting, in an algebraic representation of a computing task for a reconfigurable dataflow processor, an outer meta-pipeline loop, detecting an inner meta-pipeline loop nested within the outer meta-pipeline loop, and determining that the inner meta-pipeline loop and the outer meta-pipeline loop each conduct a common operation. The method also includes fusing the common operation for the inner meta-pipeline loop and the outer meta-pipeline loop into a single operation within the inner meta-pipeline loop. The instances of the common operation may be fused if the output of a first instance of the common operation is the source for a second instance of the common operation. Examples of the common operation include an accumulator operation, a re-read operation, and a temporal (chip buffer synchronized) operation such as a temporal concatenation operation and a temporal slicing operation.