CMD, an integrated CGRA framework featuring cluster-based distributed memory design with a co-designed compilation toolchain, is proposed, which includes a novel memory-aware mapper and a design space exploration mechanism that identifies the optimal memory architecture design for specific kernels.
Abstract
Coarse-Grained Reconfigurable Arrays (CGRAs) are a promising solution for achieving high energy efficiency and reconfigurability across various application domains, but their performance is often crippled by rigid memory architectures that limit the number and location of tiles that can access data memory. This creates a significant bottleneck for kernels with intensive memory accesses. To address this, we propose CMD, an integrated CGRA framework featuring cluster-based distributed memory design with a co-designed compilation toolchain. The compiler includes a novel memory-aware mapper and a design space exploration (DSE) mechanism that identifies the optimal memory architecture design for specific kernels. Experimental results show that our post-DSE CMD CGRAs achieve an average speedup of $1.39\times$ over a conventional CGRA while simultaneously reducing the total area to an average of $0.912\times$ of the conventional CGRA.
A multi-cluster architecture that replaces inter-cluster communication with localized data replication within ScratchPad Memories (SPMs) is introduced, creating a critical architectural trade-off between the data replication overhead required by overlapping-footprint kernels and the routing delays of larger clusters.
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