A one-shot training framework uses dynamic BN zeroization/recovery and an ML-based latency predictor to compress or scale DNNs to meet hard latency constraints on edge devices.
Abadi, et al., TensorFlow: Large-scale machine learning on heteroge- neous systems, software available from tensorflow.org (2015)
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Latency-Constrained DNN Architecture Learning for Edge Systems using Zerorized Batch Normalization
A one-shot training framework uses dynamic BN zeroization/recovery and an ML-based latency predictor to compress or scale DNNs to meet hard latency constraints on edge devices.