Patch and Model Size Characterization for On-Device Efficient-ViTs on Small Datasets Using 12 Quantitative Metrics

Vision transformers (ViTs) have emerged as a successful alternative to convolutional neural networks (CNNs) in deep learning (DL) applications for computer vision (CV), particularly excelling in accuracy on large-scale datasets within high-performance computing (HPC) or cloud domains. However, in the context of resource-constrained mobile and edge AI devices, there is a lack of systematic and comprehensive investigations into the challenging optimizations for both device-agnostic (e.g., accuracy and model size) and device-related (e.g., latency, memory usage, and power/energy consumption) multi-objectives. To resolve this problem, we first 1) introduce five device-agnostic (DA) and seven device-related (DR) quantitative metrics, 2) using which we thoroughly characterize the effects of ViT hyper-parameters on small datasets in terms of patch size and model size, and then 3) propose a simple yet effective optimization technique called the hierarchical and local (HelLo) tuning method for efficient ViTs. The results show that our method achieves significant improvements of up to 85% in MACs, 67.2% in inference latency, 77.7% in train latency/time, 63.3% in GPU memory, 73.8% in energy consumption, and 263.0% in FoM, with minimal accuracy degradation (up to 2%).