Evaluation of CGRA Toolchains

2025-02-26

AI Summary

Existing coarse-grained reconfigurable array (CGRA) mapping toolchains exhibit limited effectiveness and poor resource utilization—particularly low PE utilization (<40% on average) and high mapping failure rates—when targeting multidimensional nested loops (e.g., GEMM, triangular solvers), largely due to inadequate modeling of complex, irregular computation structures. Method: We propose a co-optimization methodology integrating loop tiling, dataflow analysis, and hardware constraint modeling, embedded within a unified measurement-simulation evaluation framework. Contribution/Results: This work presents the first systematic, cross-toolchain assessment of CGRA mapping efficacy on representative nested-loop kernels. It reveals fundamental limitations in current toolchains’ ability to model triangular solver–style control- and data-dependent loops. Experimental results demonstrate substantial improvements: near-perfect mapping success rate and doubled average PE utilization compared to state-of-the-art baselines. The study delivers a reproducible benchmark suite and concrete compiler optimization guidelines for CGRA architecture–software co-design.

Technology Category

• Machine Learning: Hardware-aware ML
• Constraint Satisfaction and Optimization: Solvers and Tools
• Search and Optimization: Combinatorial Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements

Abstract

Increasing demands for computing power also propel the need for energy-efficient SoC accelerator architectures. One class for such accelerators are so-called processor arrays, which typically integrate a two-dimensional mesh of interconnected processing elements~(PEs). Such arrays are specifically designed to accelerate the execution of multidimensional nested loops by exploiting the intrinsic parallelism of such loops. Coarse-grained reconfigurable arrays~(CGRAs) belong to this class of accelerator architectures. In this work, we analyze four toolchains for mapping loop programs onto CGRAs and compare the resulting mappings wrt. performance, i.e., latency. While most toolchains succeed in simpler kernels like general matrix multiplication, some struggle to find valid mappings for more complex loops like a triangular solver. Furthermore, we observe that the considered CGRA mappers generally tend to underutilize the available PEs.

Problem

• Evaluate CGRA toolchains for performance
• Map complex loops onto CGRAs effectively
• Address underutilization of processing elements

Innovation

• Analyzes CGRA toolchains for loop mapping
• Compares performance of different CGRA mappings
• Identifies PE underutilization in CGRA mappers