arXiv:2605.20723v1 Announce Type: new Abstract: Deploying large deep neural networks on memory-constrained mobile devices is a central challenge in edge ML. While compression, pruning, and quantization reduce per-parameter cost, transformer-based models remain too large for the 3.3-7.4 GB RAM envelope of commodity Android handsets. We present the DNN pipeline scheduling subsystem of CROWDio, which achieves practical ONNX inference across resource-constrained Android workers without model modification, by distributing memory pressure across devices via five mechanisms: JIT deferred partition lo
The proliferation of complex AI models like transformers necessitates new methods for deploying them on pervasively available edge devices, such as smartphones, which have long been constrained by memory and computational resources.
This development addresses a critical barrier to widespread AI adoption on mobile, enabling distributed, efficient deep neural network inference without requiring specialized, high-end hardware.
Mobile devices can now effectively run large AI models that were previously limited to data centers or high-end compute, shifting the paradigm for edge AI deployment from model modification to distributed resource utilization.
- · Edge AI providers
- · Android device users
- · Mobile app developers
- · Distributed computing platforms
- · Companies relying solely on cloud-based inference for mobile
- · Specialized edge AI hardware (in some use cases)
Increased capability for AI-powered features directly on commodity mobile devices.
New business models emerging around distributed mobile AI inference and crowdsourced compute.
Enhanced data privacy and reduced latency for AI applications by minimizing cloud data transfers.
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Read at arXiv cs.LG