Presentation
G2PM: Performance Modeling for ACAP Architecture with Dual-Tiered Graph Representation Learning
DescriptionPerformance estimation is a crucial component in the optimization processes of accelerator development on the Versal ACAP architecture.
However, existing approaches present limitations - they are either too slow to facilitate efficient iterations, or they lack the necessary accuracy due to the specific AIE array architecture and two-level programming model of Versal ACAP.
To tackle this challenge, we propose G$^2$PM, a performance modeling technique based on a hierarchical graph representation centered on the AIE array.
More specifically, we employ a hierarchical graph neural network to identify features of both kernel programs and dataflow programs, taking into account the hardware and software characteristics of the Versal ACAP architecture.
In our evaluations, our method demonstrates significant improvements, achieving a mean error rate of less than 1.6\% and providing a speed-up factor of 4165$\times$ compared to the simulation-based method.
However, existing approaches present limitations - they are either too slow to facilitate efficient iterations, or they lack the necessary accuracy due to the specific AIE array architecture and two-level programming model of Versal ACAP.
To tackle this challenge, we propose G$^2$PM, a performance modeling technique based on a hierarchical graph representation centered on the AIE array.
More specifically, we employ a hierarchical graph neural network to identify features of both kernel programs and dataflow programs, taking into account the hardware and software characteristics of the Versal ACAP architecture.
In our evaluations, our method demonstrates significant improvements, achieving a mean error rate of less than 1.6\% and providing a speed-up factor of 4165$\times$ compared to the simulation-based method.
Event Type
Research Manuscript
TimeTuesday, June 255:00pm - 5:15pm PDT
Location3001, 3rd Floor
AI
AI/ML Application and Infrastructure