Abstract = {Skyrocketing data volumes, growing hardware capabilities, and the revolution in machine learning (ML) theory have collectively driven the latest leap forward in ML. Despite our hope to realize the next leap with new hardware and a broader range of data, ML development is reaching scaling limits in both realms. First, the exponential surge in ML workload volumes and their complexity far outstrip hardware improvements, leading to hardware resource demands surpassing the sustainable growth of capacity. Second, the mounting volumes of edge data, increasing awareness of user privacy, and tightening government regulations render conventional ML practices, which centralize all data into the cloud, increasingly unsustainable due to escalating costs and scrutiny.
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