On device learning could dramatically improve user experience, says Qualcomm Research.
Much hope for on-device training has been pinned on a hybrid approach, pooling edge devices to aggregate model perturbations in the cloud and then distributing updated models back to the devices. Each device must adapt the model based on local data, then aggregate updates across multiple users globally for improved model performance across a broader range of observed data.
Another approach is to reduce memory requirements. Instead of storing every layer’s activations to calculate the gradient of the loss function, Qualcomm AI Research is investigating using “invertible layers” to reduce memory for training significantly. For example, MobileNet-V2 training was accomplished with an 11x reduction in activation memory, from 43MB to 3.7 MB, without a meaningful reduction in prediction accuracy or change to the number of inference FLOPS or model parameters.
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