AMA: An Analytical Approach to Maximizing the Efficiency of Deep Learning on Versal AI Engine

The traditional cache-based multi-core architecture represented by CUDA has been plagued by the “memory wall” problem, and a large number of applications represented by large language model inference are unable to meet the high computational intensity requirements. The Versal AI Engine architecture provides a variety of rich inter-core connections in the processor array, increasing many data reuse opportunities and potentially alleviating the “memory wall” issue. However, traditional parallel programming models cannot be directly applied to this architecture, and how to map computations to achieve high computational utilization becomes a new challenge. To address this, we propose AMA, a hierarchical performance analysis model built on the Versal AI Engine architecture, designed to maximize the efficiency of typical deep learning applications. Experiments show that AMA modeling is accurate and efficient. On the VCK190 platform, we achieved a matrix multiplication throughput of 5867.29 GFLOPS in fp32 and 88.55 TOPS in int8, and a convolution throughput of 99.6770 TOPS. In terms of energy efficiency, AMA achieved 142.68 GFLOPS/W in fp32 precision and 1.416 TOPS/W in int8 matrix multiplication. Compared to the current state-of-the-art methods, we achieved a $\mathbf{1 4. 9 9 \%}$ increase in throughput and a 22.92% increase in energy efficiency, providing new analytical performance model and practical guidance for efficient deep learning deployment on AI Engine.