Resource-Aware Diffusion Model Compression for Mobile and Embedded Multimodal Intelligence Systems

Abstract

Diffusion models have emerged as powerful generative frameworks capable of producing high-fidelity multimodal content that spans text, images, speech, and sensor streams. Their deployment on mobile and embedded platforms, however, is severely constrained by memory footprints, energy budgets, and real-time latency requirements that conflict with the iterative denoising process at their core. This paper presents a system-level analysis of resource-aware diffusion model compression, moving beyond isolated algorithmic contributions to examine the structural trade-offs, architectural implications, and socio-technical dimensions of shrinking these models for edge-native multimodal intelligence. We survey a continuum of compression paradigms—quantization, pruning, knowledge distillation, neural architecture search, and sampling trajectory reduction—and evaluate them through the lenses of hardware-software co-design, on-device orchestration, sustainable computing, fairness, and governance. The discussion foregrounds infrastructure-level considerations such as heterogeneous accelerator utilization, memory hierarchies, and federal learning pipelines that distribute compression across devices. By integrating case illustrations from mobile vision-language systems, on-device audio synthesis, and sensor-conditioned scene generation, we identify persistent tensions between fidelity, latency, and equity. We argue that compression must be treated not as a post-hoc optimization but as a first-class design constraint that shapes the entire training-inference-operations lifecycle. The paper concludes by outlining policy implications and future research directions for robust, inclusive, and energy-proportional diffusion intelligence at the extreme edge.