TMModel: Modeling Texture Memory and Mobile GPU Performance to Accelerate DNN Computations
Jiexiong Guan, Zhenqing Hu, Christos D. Antonopoulos, Nikolaos Bellas, Spyros Lalis, Evgenia Smirni, Gang Zhou, Gagan Agrawal, Bin Ren · 2025
The demand for Deep Neural Network (DNN) execution (including both inference and training) on mobile system-ona-chip (SoCs) has surged, driven by factors like the need for real-time latency, privacy, and reducing vendors' costs.Mainstream mobile GPUs (e.g., Qualcomm Adreno GPUs) usually have a 2.5D L1 texture cache that offers throughput superior to that of on-chip memory.However, to date, there is limited understanding of the performance features of such a 2.5D cache, which limits the optimization potential.This paper introduces TMModel, a framework with three components: 1) a set of micro-benchmarks and a novel performance assessment methodology to characterize a non-well-documented architecture with 2D memory, 2) a complete analytical performance model configurable for different data access pattern(s), tiling size(s), and other GPU execution parameters for a given operator (and associated size and shape), and 3) a compilation framework incorporating this model and generating optimized code with low overhead.TMModel is