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deep neural networks are the new standard for data processing in mobile apps in this new paradigm apps can continually evolve themselves using personalization cloud-based dnn training has been the typical approach for personalizing mobile ai but it raises private concerns and has a scalability problem own device training is an alternative concept with better privacy and scalability it simply trains the dnn module within the mobile device no sensitive data rips the user device and there is no internet and backend infrastructure dependency unfortunately dnn training can consume memory space magnitudes higher than inference operations our work sage enables own device training of modern dnn models by reducing minimum memory requirement up to 20 times while maintaining practical throughput specifically sage incorporates four different memory reduction principles in a way that memory consumption and training latency are balanced based on an analysis of the mobile platform and hardware charac
