The memory requirement is greatly diminished because of their system-algorithm co-design approach. When compared to cloud training frameworks, the suggested methods significantly reduce memory use by over a factor of 1000 and a factor of 100 compared to the best edge training framework can discover (MNN).

This framework saves energy and encourages practical use by decreasing the per-iteration time by more than 20 compared to dense update and vanilla system design. Their findings show that small IoT devices may make inferences, learn from experience, and acquire new skills over time.