PUMA: A PMU-Guided Multi-Domain Layer-Aware DVFS Framework for Low-Power Mobile AI on Smartphones

Abstract

Existing mobile GPU dynamic voltage and frequency scaling (DVFS) policies rely on coarse-grained utilization metrics and treat the GPU as an isolated control domain, failing to reflect the layer-level computational and memory diversity of deep neural network (DNN) inference. This paper proposes PUMA, a performance monitoring unit (PMU)-guided multi-domain layer-aware DVFS framework. PUMA combines offline layer/layer-chain PMU characterization with online GPU PMU observations to identify execution characteristics associated with compute-bound, memory-bound, and bursty phases. In the offline stage, representative DNN workloads were profiled across 504 frequency combinations spanning the GPU, memory interface (MIF), and internal interconnect (INT) domains to derive PMU thresholds and domain-specific frequency-correction rules. At runtime, PUMA applies threshold-based bounded corrections to the GPU, MIF, and INT frequency decisions on top of the existing governors. PUMA was implemented at the kernel level on Google Pixel 9 and evaluated using six DNN workloads. Compared with the existing governor, PUMA reduced SoC power by 28.54% on average and by up to 33.41%, while reducing energy by 23.76% on average and by up to 27.97%, with an average inference latency increase of 7.28% and a maximum increase of 12.82%. Compared with GPU-only correction, full PUMA further reduces average power by 11.4% relative to GPU-only correction, and PUMA achieves a lower energy-delay product than the existing governor across all evaluated workloads.