iOS as Acceleration (arxiv.org)

A recent study titled "iOS as Acceleration" explores the potential of utilizing iOS devices, specifically recent models with powerful processors, to enhance machine learning operations in environments with limited computational resources. The research addresses a critical barrier in the AI/ML community, which often relies on extensive compute setups or cloud solutions that are not viable in scenarios involving sensitive data or high costs. By applying distributed pipeline parallelism, the authors present a proof-of-concept system that demonstrates how iOS devices can significantly accelerate model training and batch inference in low-resource settings. This approach is significant as it highlights an innovative use of underutilized mobile technology, allowing broader access to machine learning capabilities without additional costs. The paper discusses practical applications, as well as the limitations posed by memory constraints, thermal throttling, and OS restrictions. The findings open up new avenues for research and development, suggesting that everyday mobile devices could play a pivotal role in the future of AI, democratizing access to machine learning tools and techniques for a wider range of users and applications.