Modern edge computing applications, particularly high-throughput stream processing like Vision Transformers (ViTs), demand massive spatial parallelism and efficient data movement under tight power and area constraints. Coarse-Grained Reconfigurable Architectures (CGRAs) offer a promising paradigm to balance performance, flexibility, and energy efficiency. This paper analyzes the impact of two critical CGRA design choices: processing element heterogeneity and local data reuse support. We evaluate essential computational kernels (Fast Fourier Transform (FFT) and General Matrix Multiply (GEMM)) alongside an end-to-end seizure detection transformer workload across two distinct configurations: a baseline homogeneous architecture and a heterogeneous evolution integrating specialized functional units with an Scratchpad Memory (SPM). Our evaluation demonstrates that the SPM significantly optimizes data movement, reducing memory traffic eightfold compared to a memory-less design. While the heterogeneous architecture achieves superior energy efficiency for data-shuffling tasks, the homogeneous design minimizes area overhead by 4.4x to 8.2x relative to state-of-the-art CGRAs. Furthermore, it sustains a 700 MHz operating frequency, enabling up to a 5x execution speedup over the heterogeneous configuration during matrix computations. Ultimately, this work provides an architectural roadmap for selecting CGRA fabrics based on the arithmetic intensity, performance goals, and resource envelopes of edge-scale workloads.

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