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The demand for Deep Neural Network (DNN) execution (including both inference and training) on mobile system-on-a-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 validated both on a set of DNN kernels and for training complete models on a mobile GPU, and compared against both popular mobile DNN frameworks and another GPU performance model. Evaluation results demonstrate that TMModel outperforms all baselines, achieving 1.48 − 3.61× speedup on individual kernels and 1.83 − 66.1× speedup for end-to-end on-device training with only 0.25% − 18.5% the tuning cost of the baselines.