Reconfigurable Dataflow Processor Operation Fusion for Nested Loops
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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 by detecting and fusing common operations within nested meta-pipeline loops in an algebraic representation, specifically for reconfigurable dataflow processors (RDPs), including accumulator, re-read, and temporal operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If common operations in nested meta-pipeline loops are executed separately, then the implementation is simpler, but the throughput and efficiency are lower
Solution Approach 1:
The patent merges common operations from nested meta-pipeline loops into a single fused operation. Specifically, when an inner meta-pipeline loop and an outer meta-pipeline loop both contain the same operation type (e.g., accumulator, re-read, or temporal operation), the patent combines these separate operations into one unified operation that processes data from both loops simultaneously, thereby increasing throughput while managing complexity through systematic detection and fusion rules
Solution Approach 2:
The patent creates a universal fused operation that can handle multiple functions simultaneously. The fused operation is designed to process data from both the inner and outer meta-pipeline loops using a single operation instance, making it multi-functional. This universal operation can perform accumulation, re-reading, or temporal operations on data streams from different loop levels, reducing the total number of operations needed while maintaining versatility
2Reliability
If more operations are performed in nested loops, then the computation is more complete, but the energy consumption increases
Solution Approach 1:
By merging common operations from nested loops into a single fused operation, the patent reduces the total number of operation executions. Instead of performing the same operation type multiple times across different loop levels, the fused operation processes all relevant data in one execution, thereby maintaining computation completeness while significantly reducing energy consumption associated with repeated operation executions
3Productivity
If operations are executed without fusion, then the implementation is more straightforward, but the execution time increases
Solution Approach 1:
The patent performs preliminary detection and identification of common operations between nested meta-pipeline loops before execution. The system analyzes the operation types in both inner and outer loops, identifies matching operations, and prepares the fused operation structure in advance. This preliminary action enables the system to optimize the execution path beforehand, resulting in faster execution during actual runtime while managing complexity through automated detection algorithms
Data Source
AI summary
Techniques and systems disclosed herein may relate to optimizing execution in reconfigurable dataflow processors through operation fusion. For example, a system comprising a host computer with an optimization module may be configured to conduct a method including detecting, in an algebraic representation of a computing task for a reconfigurable dataflow processor (RDP), a first pipeline loop and a second pipeline loop associated with the first pipeline loop, and determining that the first pipeline loop and the second pipeline loop each conduct a common operation. The method then may fuse the common operation for the first pipeline loop and the second pipeline loop into a fused operation and generate configuration data of the computing task for the RDP that when loaded onto an instance of one or more arrays of configurable units of the RDP, causes the one or more arrays of configurable units to implement at least the computing task.