  With the introduction of the Adaptive Intelligence Engine (AIE), the Versal
Adaptive Compute Acceleration Platform (Versal ACAP) has garnered great
attention. However, the current focus of Vitis Libraries and limited research
has mainly been on how to invoke AIE modules, without delving into a thorough
discussion on effectively utilizing AIE in its typical use cases. As a result,
the widespread adoption of Versal ACAP has been restricted. The Communication
Avoidance (CA) algorithm is considered a typical application within the AIE
architecture. Nevertheless, the effective utilization of AIE in CA applications
remains an area that requires further exploration. We propose a top-down
customized design framework, EA4RCA(Efficient AIE accelerator design framework
for regular Communication-Avoid Algorithm), specifically tailored for CA
algorithms with regular communication patterns, and equipped with AIE Graph
Code Generator software to accelerate the AIE design process. The primary
objective of this framework is to maximize the performance of AIE while
incorporating high-speed data streaming services. Experiments show that for the
RCA algorithm Filter2D and Matrix Multiple (MM) with lower communication
requirements and the RCA algorithm FFT with higher communication requirements,
the accelerators implemented by the RA4RCA framework achieve the highest
throughput improvements of 22.19x, 1.05x and 3.88x compared with the current
highest performance acceleration scheme (SOTA), and the highest energy
efficiency improvements of 6.11x, 1.30x and 7.00x.
